Dissertations / Theses on the topic 'Association rule'
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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.
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Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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Wong, Wai-kit. "Security in association rule mining." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558903.
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Wong, Wai-kit, and 王偉傑. "Security in association rule mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558903.
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REN, XIAOHUI. "COMPARING QUANTITATIVE ASSOCIATION RULE METHODS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1089133333.
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Qing, Yang. "Pruning and summarizing discovered time series association rules." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31828.
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Sensors are widely used in all aspects of our daily life including factories, hospitals and even our homes. Discovering time series association rules from sensor data can reveal the potential relationship between different sensors which can be used in many applications. However, the time series association rule mining algorithms usually produce rules much more than expected. It’s hardly to under-stand, present or make use of the rules. So we need to prune and summarize the huge amount of rules. In this paper, a two-step pruning method is proposed to reduce both the number and redundancy in the large set of time series rules. Be-sides, we put forward the BIGBAR summarizing method to summarize the rules and present the results intuitively.
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Rantzau, Ralf. "Extended concepts for association rule discovery." [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.
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Zhang, Ya Klein Cerry M. "Association rule mining in cooperative research." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6540.
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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.
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Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.
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HajYasien, Ahmed. "Preserving Privacy in Association Rule Mining." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365286.
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With the development and penetration of data mining within different fields and disciplines, security and privacy concerns have emerged. Data mining technology which reveals patterns in large databases could compromise the information that an individual or an organization regards as private. The aim of privacy-preserving data mining is to find the right balance between maximizing analysis results (that are useful for the common good) and keeping the inferences that disclose private information about organizations or individuals at a minimum. In this thesis we present a new classification for privacy preserving data mining problems, we propose a new heuristic algorithm called the QIBC algorithm that improves the privacy of sensitive knowledge (as itemsets) by blocking more inference channels. We demonstrate the efficiency of the algorithm, we propose two techniques (item count and increasing cardinality) based on item-restriction that hide sensitive itemsets (and we perform experiments to compare the two techniques), we propose an efficient protocol that allows parties to share data in a private way with no restrictions and without loss of accuracy (and we demonstrate the efficiency of the protocol), and we review the literature of software engineering related to the associationrule mining domain and we suggest a list of considerations to achieve better privacy on software.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Faculty of Engineering and Information Technology
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Marinica, Claudia. "Association Rule Interactive Post-processing using Rule Schemas and Ontologies - ARIPSO." Phd thesis, Université de Nantes, 2010. http://tel.archives-ouvertes.fr/tel-00912580.
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This thesis is concerned with the merging of two active research domains: Knowledge Discovery in Databases (KDD), more precisely the Association Rule Mining technique, and Knowledge Engineering (KE) with a main interest in knowledge representation languages developed around the Semantic Web. In Data Mining, the usefulness of association rule technique is strongly limited by the huge amount and the low quality of delivered rules. Experiments show that rules become almost impossible to use when their number exceeds 100. At the same time, nuggets are often represented by those rare (low support) unexpected association rules which are surprising to the user. Unfortunately, the lower the support is, the larger the volume of rules becomes. Thus, it is crucial to help the decision maker with an efficient technique to reduce the number of rules. To overcome this drawback, several methods have been proposed in the literature such as itemset concise representations, redundancy reduction, filtering, ranking and post-processing. Even though rule interestingness strongly depends on user knowledge and goals, most of the existing methods are generally based on data structure. For instance, if the user looks for unexpected rules, all the already known rules should be pruned. Or, if the user wants to focus on specific family of rules, only this subset of rules should be selected. In this context, we address two main issues: the integration of user knowledge in the discovery process and the interactivity with the user. The first issue requires defining an adapted formalism to express user knowledge with accuracy and flexibility such as ontologies in the Semantic Web. Second, the interactivity with the user allows a more iterative mining process where the user can successively test different hypotheses or preferences and focus on interesting rules. The main contributions of this work can be summarized as follows: (i) A model to represent user knowledge. First, we propose a new rule-like formalism, called Rule Schema, which allows the user to define his/her expectations regarding the rules through ontology concepts. Second, ontologies allow the user to express his/her domain knowledge by means of a high semantic model. Last, the user can choose among a set of Operators for interactive processing the one to be applied over each Rule Schema (i.e. pruning, conforming, unexpectedness, . . . ). (ii) A new post-processing approach, called ARIPSO (Association Rule Interactive Post-processing using rule Schemas and Ontologies), which helps the user to reduce the volume of the discovered rules and to improve their quality. It consists in an interactive process integrating user knowledge and expectations by means of the proposed model. At each step of ARIPSO, the interactive loop allows the user to change the provided information and to reiterate the post-processing phase which produces new results. (iii) The implementation in post-processing of the proposed approach. The developed tool is complete and operational, and it implements all the functionalities described in the approach. Also, it makes the connection between different elements like the set of rules and rule schemas stored in PMML/XML files, and the ontologies stored in OWL files and inferred by the Pellet reasoner. (iv) An adapted implementation without post-processing, called ARLIUS (Association Rule Local mining Interactive Using rule Schemas), consisting in an interactive local mining process guided by the user. It allows the user to focus on interesting rules without the necessity to extract all of them, and without minimum support limit. In this way, the user may explore the rule space incrementally, a small amount at each step, starting from his/her own expectations and discovering their related rules. (v) The experimental study analyzing the approach efficiency and the discovered rule quality. For this purpose, we used a real-life and large questionnaire database concerning customer satisfaction. For ARIPSO, the experimentation was carried out in complete cooperation with the domain expert. For different scenarios, from an input set of nearly 400 thousand association rules, ARIPSO filtered between 3 and 200 rules validated by the expert. Clearly, ARIPSO allows the user to significantly and efficiently reduce the input rule set. For ARLIUS, we experimented different scenarios over the same questionnaire database and we obtained reduced sets of rules (less than 100) with very low support.
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Marinica, Claudia. "Association Rule Interactive Post-processing using Rule Schemas and Ontologies : aripso." Phd thesis, Nantes, 2010. https://archive.bu.univ-nantes.fr/pollux/show/show?id=90a57cc4-245f-420d-ac2b-f9ad7929e0f7.
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Cette thèse s'inscrit à la confluence de deux domaines actifs de recherche: l'Extraction de Connaissances à partir des Données - la fouille de Règles This thesis is concerned with the merging of two active research domains: Knowledge Discovery in Databases - Association Rule Mining technique, and Knowledge Engineering - representation languages of Semantic Web. The usefulness of association rule technique is strongly limited by the huge amount and the low quality of delivered rules. To overcome this drawback, several methods have been proposed in the literature such as itemset concise representations, redundancy reduction, filtering, ranking and post-processing, and most of them are based on data structure. However, rule interestingness strongly depends on user knowledge and goals. In this context, it is crucial to help the user with an efficient technique to reduce the number of rules while keeping interesting ones. This work addresses two main issues: the integration of user knowledge in the discovery process and the interactivity with the user. The first issue requires an accurate and flexible formalism to express user knowledge such as ontologies in the Semantic Web. The second one proposes a more iterative mining process allowing the user to explore the rule space incrementally focusing on interesting rules. The main contributions of this work can be summarized as follows: (i) A model to represent user knowledge. First, we propose to represent user domain knowledge by means of ontologies. Second, we develop a new formalism, called "Rule Schema", which allows the user to define his/her expectations throughout ontology concepts. Last, we suggest the user a set of "mining Operators" to be applied over Rule Schemas. (ii) A new post-processing approach, ARJPSO. Lt allows the user to reduce the volume of the discovered rules by keeping only the interesting rules. ARIPSO is an interactive process integrating user knowledge by means of the proposed model. The interactive loop allows at each step the user to change the provided information and to reiterate the post-processing phase. (iii) The implementation in post-processing of ARJPSO. The developed tool is complete and operational, and it implements all the functionalities described in the approach. An alternative implementation, without post-processing, was proposed (ARLIUS). It consists in an interactive local mining process. (iv) An experimental study analyzing the approach efficiency and the discovered rule quality. For this purpose, we used a large real-life database; for ARJPSO, the experimentation was carried out in complete cooperation with the domain expert. From an input set of nearly 400 thousand rules, for different scenarios, ARIPSO filtered between 3 and 200 rules validated by the expert
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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.
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Thesis (M.S.) -- Worcester Polytechnic Institute.Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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Lin, Weiyang. "Association rule mining for collaborative recommender systems." Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0515100-145926.
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Hammoud, Suhel. "MapReduce network enabled algorithms for classification based on association rules." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5833.
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There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization.
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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.
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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Ahmed, Shakil. "Strategies for partitioning data in association rule mining." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415661.
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Bogorny, Vania. "Enhancing spatial association rule mining in geographic databases." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/7841.
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A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial.The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.
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Shrestha, Anuj. "Association Rule Mining of Biological Field Data Sets." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28394.
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Association rule mining is an important data mining technique, yet, its use in association analysis of biological data sets has been limited. This mining technique was applied on two biological data sets, a genome and a damselfly data set. The raw data sets were pre-processed, and then association analysis was performed with various configurations. The pre-processing task involves minimizing the number of association attributes in genome data and creating the association attributes in damselfly data. The configurations include generation of single/maximal rules and handling single/multiple tier attributes. Both data sets have a binary class label and using association analysis, attributes of importance to each of these class labels are found. The results (rules) from association analysis are then visualized using graph networks by incorporating the association attributes like support and confidence, differential color schemes and features from the pre-processed data.Bioinformatics Seed Grant Program NIH/UND
National Science Foundation (NSF) Grant IIA-1355466
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Chudán, David. "Association rule mining as a support for OLAP." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-201130.
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The aim of this work is to identify the possibilities of the complementary usage of two analytical methods of data analysis, OLAP analysis and data mining represented by GUHA association rule mining. The usage of these two methods in the context of proposed scenarios on one dataset presumes a synergistic effect, surpassing the knowledge acquired by these two methods independently. This is the main contribution of the work. Another contribution is the original use of GUHA association rules where the mining is performed on aggregated data. In their abilities, GUHA association rules outperform classic association rules referred to the literature. The experiments on real data demonstrate the finding of unusual trends in data that would be very difficult to acquire using standard methods of OLAP analysis, the time consuming manual browsing of an OLAP cube. On the other hand, the actual use of association rules loses a general overview of data. It is possible to declare that these two methods complement each other very well. The part of the solution is also usage of LMCL scripting language that automates selected parts of the data mining process. The proposed recommender system would shield the user from association rules, thereby enabling common analysts ignorant of the association rules to use their possibilities. The thesis combines quantitative and qualitative research. Quantitative research is represented by experiments on a real dataset, proposal of a recommender system and implementation of the selected parts of the association rules mining process by LISp-Miner Control Language. Qualitative research is represented by structured interviews with selected experts from the fields of data mining and business intelligence who confirm the meaningfulness of the proposed methods.
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Mahmood, Qazafi. "LC - an effective classification based association rule mining algorithm." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24274/.
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Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider.
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Baez, Monroy Vicente Oswaldo. "Neural networks as artificial memories for association rule mining." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437620.
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Fjällström, Peter. "A way to compare measures in association rule mining." Thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-124903.
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Weitl, Harms Sherri K. "Temporal association rule methodologies for geo-spatial decision support /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3091989.
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Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.
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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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Li, Jiuyong. "Optimal and Robust Rule Set Generation." Thesis, Griffith University, 2002. http://hdl.handle.net/10072/366394.
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The rapidly growing volume and complexity of modern databases makes the need for technologies to describe and summarise the information they contain increasingly important. Data mining is a process of extracting implicit, previously unknown and potentially useful patterns and relationships from data, and is widely used in industry and business applications. Rules characterise relationships among patterns in databases, and rule mining is one of the central tasks in data mining. There are fundamentally two categories of rules, namely association rules and classification rules. Traditionally, association rules are connected with transaction databases for market basket problems and classification rules are associated with relational databases for predictions. In this thesis, we will mainly focus on the use of association rules for predictions. An optimal rule set is a rule set that satisfies given optimality criteria. In this thesis we study two types of optimal rule sets, the informative association rule set and the optimal class association rule set, where the informative association rule set is used for market basket predictions and the class association rule set is used for the classification. A robust classification rule set is a rule set that is capable of providing more correct predictions than a traditional classification rule set on incomplete test data. Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. We define a rule set for a given transaction database that is significantly smaller than an association rule set but makes the same predictions as the complete association rule set. We call this rule set the informative rule set. The informative rule set is not constrained
to particular target items; and it is smaller than the non-redundant association rule set. We characterise the relationships between the informative rule set and the non-redundant association rule set. We present an algorithm to directly generate the informative rule set without generating all frequent itemsets first, and that accesses databases less often than other direct methods. We show experimentally that the informative rule set is much smaller than both the association rule set and the non-redundant association rule set for a given database, and that it can be generated more efficiently. In addition, we discuss a new unsupervised discretization method to deal with numerical attributes in general association rule mining without target specification. Based on the analysis of the strengths and weaknesses of two commonly used unsupervised numerical attribute discretization methods, we present an adaptive numerical attribute merging algorithm that is shown better than both methods in general association rule mining. Relational databases are usually denser than transaction databases, so mining on them for class association rules, which is a set of association rules whose consequences are classes, may be difficult due to the combinatorial explosion. Based on the analysis of the prediction mechanism, we define an optimal class association rule set to be a subset of the complete class association rule set containing all potentially predictive rules. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining optimal class association rule sets using upward closure properties to prune weak rules before they are actually generated. We show theoretically the efficiency of the proposed algorithm will be greater than Apriori on dense databases, and confirm experimentally that it generates an optimal class association rule set, which is very much smaller than a complete class association rule set, in significantly less time than generating the complete class association rule set by Apriori. Traditional classification rule sets perform badly on test data that are not as complete as the training data. We study the problem of discovering more robust rule sets, i.e. we say a rule is more robust than another rule set if it is able to make more accurate predictions on test data with missing attribute values. We reveal a hierarchy of k-optimal rule sets where a k-optimal rule set with a large k is more robust, and they are more robust than a traditional classification rule set. We introduce two methods to find k-optimal rule sets, i.e. an optimal association rule mining approach and a heuristic approximate approach. We show experimentally that a k-optimal rule set generated from the optimal association rule mining approach performs better than that from the heuristic approximate approach and both rule sets perform significantly better than a typical classification rule set (C4.5Rules) on incomplete test data. Finally, we summarise the work discussed in this thesis, and suggest some future research directions.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Computing and Information Technology
Science, Environment, Engineering and Technology
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Wu, Jingtong. "Interpretation of association rules with multi-tier granule mining." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71455/1/Jing_Wu_Thesis.pdf.
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This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. The experimental results indicated that the proposed approach is promising.
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Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.
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Data mining is the extraction of interesting non-trivial, implicit, previously unknown and
potentially useful information or patterns from data in large databases. Association rule
mining is a data mining method that seeks to discover associations among transactions encoded
within a database. Data mining on spatio-temporal data takes into consideration the
dynamics of spatially extended systems for which large amounts of spatial data exist, given
that all real world spatial data exists in some temporal context. We need fuzzy sets in mining
association rules from spatio-temporal databases since fuzzy sets handle the numerical
data better by softening the sharp boundaries of data which models the uncertainty embedded
in the meaning of data. In this thesis, fuzzy association rule mining is performed
on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed
for fuzzy spatio-temporal data cube construction. Besides the performance criteria
interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined
to be the metrics for the comparison of association rule mining techniques. Fuzzy association
rule mining using spatio-temporal data cubes and Apriori algorithm performed
within the scope of this thesis are compared using these metrics. Real meteorological data
(precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed
using data cube and Apriori algorithm in order to generate the fuzzy association rules.
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Jacobson, Sheri Heather. "An empirical study of the fundamental rule of free association." Thesis, City University London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435957.
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Yang, Wanzhong. "Granule-based knowledge representation for intra and inter transaction association mining." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/30398/1/Wanzhong_Yang_Thesis.pdf.
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Abstract
With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis.
Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules.
To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.
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Yang, Wanzhong. "Granule-based knowledge representation for intra and inter transaction association mining." Queensland University of Technology, 2009. http://eprints.qut.edu.au/30398/.
Full textAbstract:
Abstract
With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis.
Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules.
To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.
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Delpisheh, Elnaz, and University of Lethbridge Faculty of Arts and Science. "Two new approaches to evaluate association rules." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2530.
Full textAbstract:
Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our
second approach dynamically evaluates association rules according to a composite and
collective effect of multiple measures. It interactively snapshots the end user’s domain-
dependent requirements in evaluating association rules. In essence, our approach uses
neural networks along with back-propagation learning to capture the relative importance
of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches.viii, 85 leaves : ill. ; 29 cm
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Hahsler, Michael, Kurt Hornik, and Thomas Reutterer. "Implications of probabilistic data modeling for rule mining." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/764/1/document.pdf.
Full textAbstract:
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine associations are discussed in great detail. In this paper we investigate properties of transaction data sets from a probabilistic point of view. We present a simple probabilistic framework for transaction data and its implementation using the R statistical computing environment. The framework can be used to simulate transaction data when no associations are present. We use such data to explore the ability to filter noise of confidence and lift, two popular interest measures used for rule mining. Based on the framework we develop the measure hyperlift and we compare this new measure to lift using simulated data and a real-world grocery database.Series: Research Report Series / Department of Statistics and Mathematics
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Chang, Yu-Wen, and 張瑜紋. "Goal Association Rule." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/20163249362456871353.
Full textAbstract:
碩士淡江大學
資訊工程學系碩士在職專班
95
The association rule analysis all transaction in order to find out unknown rules between items. Mining millions transactions without specific target often fail into a corner: one is taking time process, another is more than sufficient rules.Although traditional association rule eventually will obtain association of specific target after dealing with huge unwanted datum. Acturally timely association on specific targets might be most evaluated by decision makers. For example, marketing people observe customer behavior about catalog products during new promotion activity to predict sales amount reaching profit targets.Therefore, we proposed Goal Association Rule algorithm(GAR) aim at specific targets mining desired association rule.
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Lo, Min-Lung, and 羅閔隆. "The Experience Rule for Giving Association Rules Threshold." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/43969239126914475464.
Full textAbstract:
碩士大葉大學
資訊管理學系碩士班
92
It is very important technique to find the association rule from database transactions about the data mining. What is called association rule which is to find interrelationship in a database. For the reasons the rule must be meaningful, the rule must be greater than the threshold of support and confidence. How large the threshold should be? It must be given by an expert usually. And there is no any normal regulations can be obeyed. So in our research we will try to formulate the threshold by percentile. By this method, we expect to have more meaningful association rules. In this paper, we define the threshold by the percentile. We assume the percentiles is depend on mean, skewness, kurtosis and others statistical parameter. We try to use these statistical parameters to find an experience formula, and use this experience rule may obtain optimal threshold quickly. We expect to find a using meaningfull and reliable with the experience formula.
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Liao, Yuan-Fong, and 廖原豐. "Causal Association Rule Mining." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/sy5ufc.
Full textAbstract:
碩士國立中央大學
資訊管理研究所
94
This thesis mainly probes into the causality among the investment problems of the stock market to do for the experimental subject of this research. We focus on discussing how about to promote the performance of investment. If we want to promote the performance of investment, we must understand the causality among the factor which influences the performance and performance observing value. we will utilize the method of association rule of data mining to help to look for association rules about causality among the technological indicators which influences the performance and performance observing value (ex. the reversal point of the stock price). We call these rules as Causal Association Rules. We can make these rules up into the tactics of securities trading. In the past, many scholars proposed a lot of methods of association rules, but these methods will produce a large number of large itemsets. So that there are too many rules and it is difficult to assess the interesting of rules and relatively inefficient. So we propose a CFP algorithm structure which mainly improve FP-Growth algorithm to reduce mining the unnecessary large itemsets and enable only producing the interesting causal association rules efficiently. The common data dispersed methods now have equal width interval and equal frequency interval. But when investors pass in and out stock market to buy or sell stocks, they usually reference the aggregate value of technological indicators. So we propose equal width aggregate interval and equal frequency aggregate interval. These two data dispersed methods can also support mining causal association rules with level crossing so that we can mine more interesting rules. As the result of t test, the performance of our algorithm is better than FP-growth algorithm apparently. We also find the CFP algorithm is suitable for mining large-scalar database. We arrange causal association rules in an order by different point of view to analysis so as to offer investors assistance in arrangements of investment tactics and the reference of to avoid the loss.
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Chien, Peng Wang, and 王建鵬. "Find the General Rule of Data Mining Association Rules." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08735074145658888662.
Full textAbstract:
碩士萬能科技大學
資訊管理研究所
99
At present the application of association rule mining and research, to exchange products generated discussion targeted mostly clustered, and in the exploration process and output that, there is no a general rule of representation, usually in a unique way or the text description . This study proposes a concept of transactions by participants in the association rule mining as an object. For association rule mining applications more flexible, to entities associated with the set methodology for the extension of a graphical representation, so that regardless of the implementation of the method, the can be simple and clear expression, and association rule mining to fully describe the various restrictions , regardless of entity-relationship structure, star structure, snow structure, can be described as a class can be summarized, and describe the relationship between different induction levels. Another object via the specified mining, exploration using different trading partners, meaning more like mining.
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Li, Shenzhi. "Higher order association rule mining." 2010. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3389963.
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LIN, MING-HUNG, and 林銘泓. "Exploringthe Distribution Rules of Aggregate Using Data Mining Association Rule." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/00708958833560184595.
Full textAbstract:
碩士萬能科技大學
資訊管理研究所在職專班
104
Aggregate of ready-mixed concrete from the shipping dock to bulk cargo, then vehicle distribution to various ready-mix plant, temporary storage yard. Provided that the transportation process often because there was no effective distribution rules can refer to, lead to a pier laden vehicle waiting distribution caused by congestion. This study by the association rules of data mining methods to retrieve various schedules, content delivery and distribution locations, and thus the formation of the basket, with the relevance of interrelated rules refer to find it. In this study, the use of association rules rule the aggregate distribution is obtained, only that the same timetable and distribution of goods loaded reference rule, if delivery mainland thirds stone, they will delivery six points continent stone; and distribution Hualien sand, it must distribution will Hualien Hualien sixth of stone or stone-thirds. Whereby rules can help dispatchers to quickly make a correct and efficient delivery schedule, another of the study were not included because of the time it is not possible depth information delivery order.
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Cowen, Nicholas L. "Universal Design Rules from Product Pairs and Association Rule Based Learning." 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7964.
Full textAbstract:
A product pair is two products with similar functionality that satisfy the same
high level need but are different by design. The goal of this research is to apply
association rule-based learning to product pairs and develop universal design rules to be
used during the conceptual design phase. The Apriori algorithm produced 1,023
association rules with input parameters of 70% minimum confidence and 0.5%
minimum support levels. These rules were down-selected based on the prescribed rule
format of: (Function, Typical User Activity) ? (Change, Universal User Activity). In
other words, for a given product function and user activity, the rules suggest a design
change and new user activity for a more universal product.
This research presents 29 universal design rules to be used during the conceptual
design stage. These universal design rules suggest a parametric, morphological,
functional, or no design change is needed for a given user activity and product function.
No design change rules confirm our intuition and also prevent inefficient design efforts.
A parametric design change is suggested for actionfunction elements involving find hand
use to manipulate a product. Morphological design changes are proposed to solve actionfunction elements in a slightly more complex manner without adding or
subtracting overall functionality. For example, converting human energy to mechanical
energy with the upper body opposed to the lower body or actuating fluid flow with
motion sensors instead of manual knobs. The majority of the recommended functional
changes involve automating a product to make it more universal which might not be
apparently obvious to designers during conceptual design.
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Lin, Shih Hsiang, and 林士翔. "DARM: Doughnut-shaped Association Rule Mining." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/54386438560648611106.
Full textAbstract:
碩士長庚大學
資訊管理學研究所
97
This is the age of “Information Explosion”. We can easier to get more and more information. Information visualization research is to be valuable for conveniently presenting the infinite information. It is often seen the information visualization products like maps, signs, graphs in our life. Information visualization can also use in data mining methodology. Data mining is often called knowledge discovery. Association rule mining is the most famous data mining method. Association rule mining is used to discover all associations among items. However, user can not hold the important item fast and exactly by text. We propose an association rule algorithm which use doughnut shapes to present association rule. DARM(Doughnut-shaped association rule mining) includes a overview circle and lots of detail circles which produced by items. DARM let user understand the mining step easily. User can use their self-knowledge and self-experience to participate in the process. Most importantly, we use the simple and clear doughnut shapes let user realize the database overview and all associations among items rapidly.
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Chen, Yi-Ling, and 陳依伶. "Developing an Optimal Fuzzy Association Rule Algorithm." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/35075325825710983294.
Full textAbstract:
碩士元智大學
工業工程與管理學系
93
Association rule is one of the most often discussed data mining technology. It is used in market basket analysis to know the regularity of customer’s purchase behavior. Although association rule is popular, it is limited to the item with categorical value. To solve the difficulty, this research develops an optimal fuzzy association rule algorithm so that the items with numerical data values can also be applied. First, linguistic sets of each attribute are encoded as genes of a chromosome. The optimal fuzzy membership functions are generated automatically after a serous of genetic evolution. Then, the fuzzy transaction data-mining algorithm (FTDA) is used to produce fuzzy association rules. Finally, testing data is used to evaluate the accuracy of generated fuzzy association rules. Through a series of experiments, it is shown that the algorithm is suitable for items with numerical data and performs high forecast accuracy.
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Kao-Lun, Shiao, and 蕭國倫. "An Association Rule Algorithm for Direct Marketing." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/60850826916866031958.
Full textAbstract:
碩士國立臺灣科技大學
資訊管理系
92
Direct marketing is an important application of the data mining. To acquire new customers, a company will use historical data about customers to build a model for selecting new customers. Conventional direct marketing uses response rate as the sole criterion for customer selection; that is, the company will select a potential customer for a marketing campaign based on the probability for the potential customer to respond. While this approach guarantees to render high response rate, it needs not guarantee to get high gross profit for the company. This is because some potential customer with high response rate may contribute only a little profit to the company. In this thesis, we proposed a new profit-based customer selection approach. This approach not only considers the customer response rate, but also considers the value of the customer to the company. Experiments show that the proposed approach can select high value customer so as to increase the gross profit of the company.
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Hsieh, Tzung-Han, and 謝宗翰. "An algorithm for disjunctive consequent association rule." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/31956895025580804642.
Full textAbstract:
碩士淡江大學
資訊工程學系
92
In recent years, with constant innovation and advancement of science, computers have already been used to a large extent among all businesses. Due to the fast development and popularization of Internet and WWW, it is more convenient than before to get information. In such a situation that there is larger and larger amount of materials, a lot of organizations store and manage these materials by the database system. When database grows more and more huge day by day, how to grab commercial intelligence which is available but difficultly found out from the large-scale materials has already been an important subject for research. Data mining is now an important technology to search for knowledge from database. The association rule is one important part of data mining, and its major function is to detect the relationship of each item in order to discover the worthful rule.Generally speaking, only when both of the support and the confidence of the two association rules A→B and A→C are over the minimum degree will they are useful. But in true life, the conditions may be not the same. The less degree of support may mean that the item A is the later product. Furthermore, when the confidence of the A→B and A→C doesn’t reach the minimum degree, we are not able to be sure that the confidence of A→B∪C won’t reach it. In fact, if the confidence of A→B∪C is over the minimum degree and the item A is a new product, A→B∪C will be a very useful rule.Therefore, this thesis introduces a new algorithm, which is to detect these useful rules for this situation. The former of these rules is characterized in special unit form and the latter of them is characterized in disjunctive form, so we call the rule disjunctive consequent association rule.
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Teng, Ming-Jung, and 鄧明容. "A Study of Association Rule Searching Algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/18189835564740578212.
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碩士國立臺灣科技大學
自動化及控制研究所
91
Data mining technique is a popular field, especially in finding the association rules among the data items in. This information can be used to assist the users to discover the hidden knowledge. This research was addressed on a POS (Point of Sale) database. It was found that many algorithms were not suitable for the situation when average size of transactions is long. Therefore, this work was addressed on developing an algorithm PMFI (Partition Maximum Frequent Itemsets) for long average size of transactions. PMFI algorithm modifies Pincer-Search algorithm by adding a partition algorithm. In another word, PMFI algorithm first partitions database into two, and each district gets frequent itemsets by Pincer-Search algorithm, and get maximum frequent itemsets by intersection and non —intersection.
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Wang, Shi-Jung, and 王錫中. "Applying Association Rule Technique to Product Design." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/27254500913047505814.
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碩士元智大學
工業工程與管理學系
90
With the help of advanced technology, product life cycle becomes shorter and shorter. Concurrent Engineering (CE), contrast to Sequential Engineering, is a product development paradigm that considers all product life cycle activities at a time to shorten design phase and lower the cost. The activities include manufacturing, assembling, reliability, and recycling. Although CE can condense time-to-market and increases competitiveness of new products, it is found that current CE practice is not enough in customer-oriented, so the design of product can’t satisfy customers’ requirements. To solve the described problems, this research applies association rule technique to analyze the customer’s preference from different product combination of the market. Meanwhile, since the new customer purchase data occurred constantly, this research applies Neural Networks to integrate old rules with new rules. Proposed system can feedback dynamic market information to the designer so that Quick Response (QR) can be achieved.
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Lee, Cho-Ming, and 李卓銘. "Classifying Chinese Text Documents by Association rule." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/53570890388178573247.
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碩士淡江大學
資訊工程學系碩士在職專班
95
Use improved TFIDF to build weighting table. Thereby, the system computes the sum of weight of each document relative to each category. According to this way, we can classify the documents which haven’t been labeled. In this paper, we use improve TFIDF to calculate the keywords weight and then combine two words as a new word by association rule to help us increase the keywords. We exploit association rule technology to apply to the data mining miner. The features of weight table are input into the data mining miner and examined whether these rules sorted by confidence, support and the length of rule to save into rule base. It will make the classification more efficiency.
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Cheng, Yung-Hsiung, and 鄭永雄. "A study of association rule mining algorithms." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/12205682895999423189.
Full textAbstract:
碩士義守大學
資訊管理學系碩士班
95
In recent years, the techniques of Data Mining has already become one of the rather popular research subjects. Its purpose is to mining meaningful information from the database, and provides it to the administrator for decision making. In past relevant research, many algorithms were proposed to improve the effect of association rule currently. These methods are to reduce the computation of non-correlation itemsets to save the CPU time, or to reduces the information search frequency to save the I/O cost, or even to improve storage configuration and access method to promote whole effect. These algorithms each have their own advantage. but lack of synthetically inter-communication. If the user is to mining an unknown database, it will be difficult to determine which algorithm provides the best effect, therefore we must consider the applicability of the association rule of data mining algorithm in order to mine data more effectively and obtain useful information. The research inquires into presently five association rule algorithms, and uses them individually to process several real databases. And then analyze these experiment data to see each algorithm’s pros and cons and its applicable type of database characteristics. We then carry on to process the Apriori algorithm, Frequent-pattern growth(FP-growth) algorithm, Dynamic Itemset Counting(DIC) algorithm, the Pruning of the Direct Hashing(DHP) algorithm and the LCM-freq algorithm according to the characteristic of database, obtain the processed data from several database and organize them. Finally, we wish to suggest the users use more effective association rules of data mining algorithm.
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Wang, Tzu-Yuan, and 王咨淵. "An Association Classification Rule Based Rule extraction Algorithm for Competitive Learning Neural Networks." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/40730691213108229467.
Full textAbstract:
碩士國立臺灣大學
工業工程學研究所
93
Neural networks have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful for function approximation problems because they have been shown to be universal approximators. But, The neural network is considered a black box. It is hard to determine if the learning result of a neural network is reasonable, and the network can not effectively help users to develop the domain knowledge. Thus, it is important to supply a reasonable and effective analytic method of the neural network. This research expects to be able to improve the black box shortcoming of the solving type neural network. Competitive Learning Neural Network include Self-Organized Map, Learning Vector Quantization. These common characteristics of network are that are all to adopt the single layer of neural networks that Winner-Take-All completely that their study rules .However, past researchs are mostly all limited on the neural network structure of the feedforward network, but the important degree that can''t know this rule. So this research develop to extract out the Association Classification Rule from neurons. Finally, extracted rule is compared decision tree-C4.5, proves with some BenchMark Problems in UCI Machine Learning DataBase that distinguish the correct rate.
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Jin, Weiqing. "Fuzzy classification based on fuzzy association rule mining." 2004. http://www.lib.ncsu.edu/theses/available/etd-12072004-130619/unrestricted/etd.pdf.
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Fan, Chih-Ping, and 范治平. "Improve web service performance base on association rule." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/48427343205306398966.
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碩士萬能科技大學
資訊管理與數位商業研究所在職專班
98
The Service Oriented Architecture develops the existing system's utilization scope using the network service, the suitable reorganization existing system function becomes the function mold train, will apply the service guidance construction to be possible again to promote the network service potency and to reduce the whole use cost effectively, this research will develop one kind using the material exploration's connection rule to be possible to discover the most suitable function mold train method, we will utilize it on the actual system the result to confirm that it may promote the network service potency and reduce the whole use cost effectively.