Статті в журналах: "Rules database"

1

Bonam, Janakiramaiah, and Ramamohan Reddy. "Balanced Approach for Hiding Sensitive Association Rules in Data Sharing Environment." International Journal of Information Security and Privacy 8, no. 3 (July 2014): 39–62. http://dx.doi.org/10.4018/ijisp.2014070103.

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Privacy preserving association rule mining protects the sensitive association rules specified by the owner of the data by sanitizing the original database so that the sensitive rules are hidden. In this paper, the authors study a problem of hiding sensitive association rules by carefully modifying the transactions in the database. The algorithm BHPSP calculates the impact factor of items in the sensitive association rules. Then it selects a rule which contains an item with minimum impact factor. The algorithm alters the transactions of the database to hide the sensitive association rule by reducing the loss of other non-sensitive association rules. The quality of a database can be well maintained by greedily selecting the alterations in the database with negligible side effects. The BHPSP algorithm is experimentally compared with a HCSRIL algorithm with respect to the performance measures misses cost and difference between original and sanitized databases. Experimental results are also mentioned demonstrating the effectiveness of the proposed approach.

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2

Ceri, Stefano, and Raghu Ramakrishnan. "Rules in database systems." ACM Computing Surveys 28, no. 1 (March 1996): 109–11. http://dx.doi.org/10.1145/234313.234362.

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3

B., Suma, and Shobha G. "Privacy preserving association rule hiding using border based approach." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (August 1, 2021): 1137. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.

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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

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4

Alotaibi, Obaid, and Eric Pardede. "Transformation of Schema from Relational Database (RDB) to NoSQL Databases." Data 4, no. 4 (November 27, 2019): 148. http://dx.doi.org/10.3390/data4040148.

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Relational database has been the de-facto database choice in most IT applications. In the last decade there has been increasing demand for applications that have to deal with massive and un-normalized data. To satisfy the demand, there is a big shift to use more relaxed databases in the form of NoSQL databases. Alongside with this shift, there is a need to have a structured methodology to transform existing data in relational database (RDB) to NoSQL database. The transformation from RDB to NoSQL database has become more challenging because there is no current standard on NoSQL database. The aim of this paper is to propose transformation rules of RDB Schema to various NoSQL database schema, namely document-based, column-based and graph-based databases. The rules are applied based on the type of relationships that can appear in data within a database. As a proof of concept, we apply the rules into a case study using three NoSQL databases, namely MongoDB, Cassandra, and Neo4j. A set of queries is run in these databases to demonstrate the correctness of the transformation results. In addition, the completeness of our transformation rules are compared against existing work.

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5

Hanson, Eric N., and Jennifer Widom. "An overview of production rules in database systems." Knowledge Engineering Review 8, no. 2 (June 1993): 121–43. http://dx.doi.org/10.1017/s0269888900000126.

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AbstractDatabase researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.

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Al-Khafaji, Hussien, Alaa Al-Hamami, and Abbas F. Abdul-Kader. "Design and Implementation of a Generator of Large , Dense ,or Sparse Databases to Test Association Rules Miner." Iraqi Journal for Computers and Informatics 40, no. 1 (December 31, 2002): 25–31. http://dx.doi.org/10.25195/ijci.v40i1.223.

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Association rules discovery has emerged as a very important problem in knowledge discovery in database and data mining. A number of algorithms is presented to mine association rules. There are many factors that affect the efficiency of rules mining algorithms, such as largeness, denances, and sparseness of databases used to be mined, in addition to number of items, number and average sizes of transactions, number and average sizes of frequent itemscts, and number and average sizes of potentially maximal itemsets. It is impossible to change present realworld catabase's characteristics to fairly test and determine the best and wurst cases of rule-mining algorithms. to be efficiently used for present and future databases. So the researchers attend to construct artificial database to qualitative and quantitative presence of the above mentioned factors to test the efficiency of rule mining algorithms and programs. The construction of such databases CATmes very large amount of the and efforts. This resent presents a software system, generator, to construct artificial databases.

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Bai, Yi Ming, Xian Yao Meng, and Xin Jie Han. "Mining Fuzzy Association Rules in Quantitative Databases." Applied Mechanics and Materials 182-183 (June 2012): 2003–7. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.2003.

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In this paper, we introduce a novel technique for mining fuzzy association rules in quantitative databases. Unlike other data mining techniques who can only discover association rules in discrete values, the algorithm reveals the relationships among different quantitative values by traversing through the partition grids and produces the corresponding Fuzzy Association Rules. Fuzzy Association Rules employs linguistic terms to represent the revealed regularities and exceptions in quantitative databases. After the fuzzy rule base is built, we utilize the definition of Support Degree in data mining to reduce the rule number and save the useful rules. Throughout this paper, we will use a set of real data from a wine database to demonstrate the ideas and test the models.

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Aiken, Alexander, Jennifer Widom, and Joseph M. Hellerstein. "Behavior of database production rules." ACM SIGMOD Record 21, no. 2 (June 1992): 59–68. http://dx.doi.org/10.1145/141484.130296.

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9

Gopagoni, Praveen Kumar, and Mohan Rao S K. "Distributed elephant herding optimization for grid-based privacy association rule mining." Data Technologies and Applications 54, no. 3 (May 15, 2020): 365–82. http://dx.doi.org/10.1108/dta-07-2019-0104.

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PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

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10

Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.

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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

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11

Orman, Levent V. "Production Rules for General Database Users." Journal of Database Management 1, no. 2 (July 1990): 18–29. http://dx.doi.org/10.4018/jdm.1990100102.

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12

Gento, Angel M. "Decision rules for a maintenance database." Journal of Quality in Maintenance Engineering 10, no. 3 (September 2004): 210–20. http://dx.doi.org/10.1108/13552510410553262.

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13

Motakis, Iakovos, and Carlo Zaniolo. "Temporal aggregation in active database rules." ACM SIGMOD Record 26, no. 2 (June 1997): 440–51. http://dx.doi.org/10.1145/253262.253359.

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14

Ioannidis, Yannis E., and Timos K. Sellis. "Supporting inconsistent rules in database systems." Journal of Intelligent Information Systems 1, no. 3-4 (December 1992): 243–70. http://dx.doi.org/10.1007/bf00962920.

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15

Gayathiri, P., and B. Poorna. "Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database." Cybernetics and Information Technologies 17, no. 3 (September 1, 2017): 92–108. http://dx.doi.org/10.1515/cait-2017-0032.

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Abstract Association Rule Hiding methodology is a privacy preserving data mining technique that sanitizes the original database by hide sensitive association rules generated from the transactional database. The side effect of association rules hiding technique is to hide certain rules that are not sensitive, failing to hide certain sensitive rules and generating false rules in the resulted database. This affects the privacy of the data and the utility of data mining results. In this paper, a method called Gene Patterned Association Rule Hiding (GPARH) is proposed for preserving privacy of the data and maintaining the data utility, based on data perturbation technique. Using gene selection operation, privacy linked hidden and exposed data items are mapped to the vector data items, thereby obtaining gene based data item. The performance of proposed GPARH is evaluated in terms of metrics such as number of sensitive rules generated, true positive privacy rate and execution time for selecting the sensitive rules by using Abalone and Taxi Service Trajectory datasets.

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Agrawal, Shivangee, and Nivedita Bairagi. "A Survey for Association Rule Mining in Data Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 245. http://dx.doi.org/10.23956/ijarcsse.v7i8.58.

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Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.

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Pandey, Anjana, and K. R. Pardasani. "PPCI Algorithm for Mining Temporal Association Rules in Large Databases." Journal of Information & Knowledge Management 08, no. 04 (December 2009): 345–52. http://dx.doi.org/10.1142/s0219649209002440.

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In this paper an attempt has been made to develop a progressive partitioning and counting inference approach for mining association rules in temporal databases. A temporal database like a sales database is a set of transactions where each transaction T is a set of items in which each item contains an individual exhibition period. The existing models of association rule mining have problems in handling transactions due to a lack of consideration of the exhibition period of each individual item and lack of an equitable support counting basis for each item. As a remedy to this problem we propose an innovative algorithm PPCI that combines progressive partition approach with counting inference method to discover association rules in a temporal database. The basic idea of PPCI is to first segment the database into sub-databases in such a way that items in each sub-database will have either a common starting time or a common ending time. Then for each sub-database, PPCI progressively filters 1-itemset with a cumulative filtering threshold based on vital partitioning characteristics. Algorithm PPCI is also designed to employ a filtering threshold in each partition to prune out those cumulatively infrequent 1-itemsets early and it also uses counting inference approach to minimise as much as possible the number of pattern support counts performed when extracting frequent patterns. Explicitly the execution time of PPCI in order of magnitude is smaller than those required by the schemes which are directly extended from existing methods.

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Öztürk, Ahmet Cumhur, and Belgin Ergenç. "Dynamic Itemset Hiding Algorithm for Multiple Sensitive Support Thresholds." International Journal of Data Warehousing and Mining 14, no. 2 (April 2018): 37–59. http://dx.doi.org/10.4018/ijdwm.2018040103.

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This article describes how association rule mining is used for extracting relations between items in transactional databases and is beneficial for decision-making. However, association rule mining can pose a threat to the privacy of the knowledge when the data is shared without hiding the confidential association rules of the data owner. One of the ways hiding an association rule from the database is to conceal the itemsets (co-occurring items) from which the sensitive association rules are generated. These sensitive itemsets are sanitized by the itemset hiding processes. Most of the existing solutions consider single support thresholds and assume that the databases are static, which is not true in real life. In this article, the authors propose a novel itemset hiding algorithm designed for the dynamic database environment and consider multiple itemset support thresholds. Performance comparisons of the algorithm is done with two dynamic algorithms on six different databases. Findings show that their dynamic algorithm is more efficient in terms of execution time and information loss and guarantees to hide all sensitive itemsets.

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Pal, Parashu Ram, Pankaj Pathak, and Shkurte Luma-Osmani. "IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification." Journal of Information & Knowledge Management 20, no. 01 (March 2021): 2150010. http://dx.doi.org/10.1142/s0219649221500106.

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Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

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Zhu, Tian Xiang, Xiao Lan Tian, Shu Hui Sun, and Shu Jie Sun. "Discovery Algorithm of the Association Rules Based on Cloud Database." Applied Mechanics and Materials 543-547 (March 2014): 3569–72. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3569.

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Cloud computing is the latest trend in IT technical development, the importance of cloud databases has been widely acknowledged. There are numerous data in the cloud database and among these data, much potential and valuable knowledge are implicit. The key point is to discover and pick up the useful knowledge automatically. An association rule is one of the main models in mining out these data, and it mainly focuses on the relationship among different areas in the data. This paper puts forward the basic model of data mining based on association rules in cloud database and introduces corresponding mining algorithms.

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Qu, Zhen Xin. "Mining Implied Semantics of Database." Advanced Engineering Forum 1 (September 2011): 38–41. http://dx.doi.org/10.4028/www.scientific.net/aef.1.38.

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To mine richer semantics from relational database data, method of mining was discussed. Aiming at specific type of semantics, thirteen rules were proposed. Applying these rules some implied semantics was found naturally. The research shows that these rules have good maneuverability and high efficiency.

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Apiletti, Daniele, Giulia Bruno, Elisa Ficarra, and Elena Baralis. "Data Cleaning and Semantic Improvement in Biological Databases." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 219–29. http://dx.doi.org/10.1515/jib-2006-40.

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Summary Public genomic and proteomic databases can be affected by a variety of errors. These errors may involve either the description or the meaning of data (namely, syntactic or semantic errors). We focus our analysis on the detection of semantic errors, in order to verify the accuracy of the stored information. In particular, we address the issue of data constraints and functional dependencies among attributes in a given relational database. Constraints and dependencies show semantics among attributes in a database schema and their knowledge may be exploited to improve data quality and integration in database design, and to perform query optimization and dimensional reduction. We propose a method to discover data constraints and functional dependencies by means of association rule mining. Association rules are extracted among attribute values and allow us to find causality relationships among them. Then, by analyzing the support and confidence of each rule, (probabilistic) data constraints and functional dependencies may be detected. With our method we can both show the presence of erroneous data and highlight novel semantic information. Moreover, our method is database-independent because it infers rules from data. In this paper, we report the application of our techniques to the SCOP (Structural Classification of Proteins) and CATH Protein Structure Classification databases.

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Jiang, Baoqing, Xiaohua Hu, Qing Wei, Jingjing Song, Chong Han, and Meng Liang. "Weak Ratio Rules." International Journal of Data Warehousing and Mining 7, no. 3 (July 2011): 50–87. http://dx.doi.org/10.4018/jdwm.2011070103.

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This paper examines the problem of weak ratio rules between nonnegative real-valued data in a transactional database. The weak ratio rule is a weaker form than Flip Korn’s ratio rule. After analyzing the mathematical model of weak ratio rules problem, the authors conclude that it is a generalization of Boolean association rules problem and every weak ratio rule is supported by a Boolean association rule. Following the properties of weak ratio rules, the authors propose an algorithm for mining an important subset of weak ratio rules and construct a weak ratio rule uncertainty reasoning method. An example is given to show how to apply weak ratio rules to reconstruct lost data, and forecast and detect outliers.

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Han, Kyong Rok, and Jae Yearn Kim. "FCILINK: Mining Frequent Closed Itemsets Based on a Link Structure between Transactions." Journal of Information & Knowledge Management 04, no. 04 (December 2005): 257–67. http://dx.doi.org/10.1142/s0219649205001213.

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The problem of discovering association rules between items in a database is an emerging area of research. Its goal is to extract significant patterns or interesting rules from large databases. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm called FCILINK is proposed that is based on a link structure between transactions. A given database is scanned once and then a much smaller sub-database is scanned twice. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.

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Byon, Lu Na, and Jeong Hye Han. "Fast Algorithms for Temporal Association Rules in a Large Database." Key Engineering Materials 277-279 (January 2005): 287–92. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.287.

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As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

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Shimada, Kaoru, and Takashi Hanioka. "An Evolutionary Method for Associative Contrast Rule Mining from Incomplete Database." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 766–77. http://dx.doi.org/10.20965/jaciii.2015.p0766.

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We propose a method for associative contrast rule mining from an incomplete database to find interesting differences between two incomplete datasets. The associative contrast rule is defined as follows: although an association rule “if X then Y” satisfies the given importance conditions within Database A, the same rule does not satisfy the same conditions within Database B. The proposed method extracts associative contrast rules directly without generating the frequent itemsets used in conventional rule mining methods. We developed our message using the basic evolutionary graph-based optimization basic structure and a new evolutionary strategy for rule accumulation mechanism. The method realizes association analysis between two classes of an incomplete database using the chi-square test. We evaluated the performance of the method for associative contrast rule mining from the incomplete database. Experimental results showed that our proposed method extracts associative contrast rules effectively. Evaluations of the mischief for rule measurements by missing values are demonstrated. Simulation results showed the difference between using the proposed method for an incomplete database and using the database as complete.

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Sun, Qiuhong, Junhai Zhang, and Xinhang Xu. "Research and Application of Rule Updating Mining Algorithm for Marine Water Quality Monitoring Data." Polish Maritime Research 25, s3 (December 1, 2018): 136–40. http://dx.doi.org/10.2478/pomr-2018-0122.

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Abstract This paper studies the characteristics of marine water quality monitoring data monitored by photoelectric sensor network, mines the potential information from the massive data. on account of the continuous accumulation of monitoring data, this paper focuses on the study of database with numerical attribute and proposes a rule updating algorithm for solving the rule maintenance issues caused by changes in the database. according to the rule, the algorithm forms a new database from part of the original data and the new data, and searches the new database by random search, thus can avoid creating a large number of redundant rules and can quickly mine effective rules at the same time. experimental results show that this method not only can avoid mining in the whole original massive data, but also can improve work efficiency, and can quickly and effectively find new data and find useful rules in the data with high practicability.

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Fouad, Mohammed M., Mostafa G. M. Mostafa, Abdulfattah S. Mashat, and Tarek F. Gharib. "IMIDB: An Algorithm for Indexed Mining of Incremental Databases." Journal of Intelligent Systems 26, no. 1 (January 1, 2017): 69–85. http://dx.doi.org/10.1515/jisys-2015-0107.

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AbstractAssociation rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size.

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Gogou, G., P. D. Bamidis, I. Vlahavas, N. Maglaveras, and S. Konias. "Predicting Missing Values in a Home Care Database Using an Adaptive Uncertainty Rule Method." Methods of Information in Medicine 44, no. 05 (2005): 639–46. http://dx.doi.org/10.1055/s-0038-1634020.

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Summary Objectives: Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. Methods: In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. Results: The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record’s attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. Conclusions: It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

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BLANCO, IGNACIO J., MARIA J. MARTIN-BAUTISTA, OLGA PONS, and M. AMPARO VILA. "A TUPLE-ORIENTED ALGORITHM FOR DEDUCTION IN A FUZZY RELATIONAL DATABASE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, supp01 (September 2003): 47–66. http://dx.doi.org/10.1142/s0218488503002260.

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In this paper, we define the concept of generalized rule for making classical deduction with imprecise data, stored both data and rules in a fuzzy relational database represented in the GEFRED model. We propose a way of measuring the imprecision related to the calculation of a fact based on the matching degree of the facts in the database and the facts calculated while expanding the rules. In order to achieve this, classical algorithms for deduction are not appropriated and we propose the modifications that have to be applied on a classical tuple-oriented algorithm in order to design a new algorithm for deducing from imprecise data with generalized rules.

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31

Tan, C. W., and A. Goh. "Implementing ECA rules in an active database." Knowledge-Based Systems 12, no. 4 (August 1999): 137–44. http://dx.doi.org/10.1016/s0950-7051(99)00028-3.

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32

Zhou, Huiyu, Wei Wei, Kaoru Shimada, Shingo Mabu, and Kotaro Hirasawa. "Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (September 20, 2008): 467–78. http://dx.doi.org/10.20965/jaciii.2008.p0467.

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In this paper, we propose a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attributes accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. GNP, a kind of evolutionary computation, represents solutions using graph structures. Because of the inherent features of GNP, it works well in dynamic environments. In this paper, GNP is applied to generate candidate association rules using the database consisting of a large number of time related attributes. In order to deal with a large number of attributes, GNP individual accumulates fitter attributes gradually during rounds, and the rules of each round are stored in a Small Rule Pool using a hash method, then, the rules are finally stored in a Big Rule Pool after the check of the overlap at the end of each round. The aim of this paper is to better handle association rule extraction of the databases in a variety of time-related applications, especially in the traffic prediction problems. The algorithm which can find the important time related association rules is described and several experimental results are presented considering a traffic prediction problem.

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33

Shimada, Kaoru, and Kotaro Hirasawa. "Flexible Rule Mining for Difference Rules and Exception Rules from Incomplete Database." IEEJ Transactions on Electronics, Information and Systems 130, no. 10 (2010): 1873–81. http://dx.doi.org/10.1541/ieejeiss.130.1873.

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34

Jain, Deepti, and Divakar Singh. "A Review on associative classification for Diabetic Datasets A Simulation Approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 21, 2013): 533–38. http://dx.doi.org/10.24297/ijct.v7i1.3483.

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Association rules are used to discover all the interesting relationship in a potentially large database. Association rule mining is used to discover a small set of rules over the database to form more accurate evaluation. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. This review paper aims to study and observe a flexible way, of, mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural Network Association Classification system is also analyzed here to be one of the approaches for building accurate and efficient classifiers. In this review paper, the Neural Network Association Classification system is studied and compared in order to find best possible accurate results. Association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules. This review research paper also analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting Diabetes.

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35

Zhang, Xiao Lin, Wan Li Wang, and Xiao Qi Lv. "Research on Technology of Medical Image Database and its Connection with HIS Database." Advanced Materials Research 267 (June 2011): 119–23. http://dx.doi.org/10.4028/www.scientific.net/amr.267.119.

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Medical digital image information storage standard and existing heterogeneous database technology were analyzed with view to the particularity of the standard for medical images; data in medical images were organized according to data organizational hierarchy in the DICOM standard and sophisticated relational databases were utilized to store medical images. Using XML as a middleware to connect medical image database to HIS and then through relational databases and XML transformation rules to complete the conversion of the two databases into XML document. The study indicated that the method is able to achieve the purpose of the connection of heterogeneous databases.

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36

Artiemjew, Piotr, Lada Rudikova, and Oleg Myslivets. "About Rule-Based Systems: Single Database Queries for Decision Making." Future Internet 12, no. 12 (November 27, 2020): 212. http://dx.doi.org/10.3390/fi12120212.

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One of the developmental directions of Future Internet technologies is the implementation of artificial intelligence systems for manipulating data and the surrounding world in a more complex way. Rule-based systems, very accessible for people’s decision-making, play an important role in the family of computational intelligence methods. The use of decision-making rules along with decision trees are one of the simplest forms of presenting complex decision-making processes. Decision support systems, according to the cross-industry standard process for data mining (CRISP-DM) framework, require final embedding of the learned model in a given computer infrastructure, integrated circuits, etc. In this work, we deal with the topic concerning placing the learned rule-based model of decision support in the database environment-exactly in the SQL database tables. Our main goal is to place the previously trained model in the database and apply it by means of single queries. In our work we assume that the decision-making rules applied are mutually consistent and additionally the Minimal Description Length (MDL) rule is introduced. We propose a universal solution for any IF THEN rule induction algorithm.

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37

Miftakul Amin, M., Andino Maseleno, Shankar K, Eswaran Perumal, R. M Vidhyavathi, and Lakshmanaprabu SK. "Active Database System Approach and Rule Based in the Development of Academic Information System." International Journal of Engineering & Technology 7, no. 2.26 (May 7, 2018): 95. http://dx.doi.org/10.14419/ijet.v7i2.26.14361.

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Active database system is a database system which is capable of generating a certain action automatically if it detects an event that meets certain conditions. The existence of Event Condition Action (ECA) rules and functional components such as triggers, stored procedures, and stored functions that are owned by active database system make the database system has the ability to automatically monitor input and output data. Separation of ECA rules components in the database with the application program will also facilitate the development of information systems. This research applies active database system in academic information system, so that academic business rules can be planted in database software and be able to produce the right solution automatically. The addition of active database system components to the database software makes procedures such as subject distribution, study plans, academic leave, values, thesis defense and other processes can be monitored by the system automatically. This can be done because between applications and databases use the model driven approach parameters to communicate with each other. The results of this research prove that a database is not only has function as a container of data, but can control the information system actively, this is caused by the logic of programs that are generally planted in the application layer that can be moved and planted in the database software.

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38

Aisaka, Kazuki, Masahiko Tsukamoto, Kaname Harumoto, and Shojiro Nishio. "On selection rules for database compression using KDD." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 84, no. 11 (2001): 11–20. http://dx.doi.org/10.1002/ecjc.1044.

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39

Widom, Jennifer, and S. J. Finkelstein. "Set-oriented production rules in relational database systems." ACM SIGMOD Record 19, no. 2 (May 1990): 259–70. http://dx.doi.org/10.1145/93605.98735.

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40

Major, John A., and John J. Mangano. "Selecting among rules induced from a hurricane database." Journal of Intelligent Information Systems 4, no. 1 (January 1995): 39–52. http://dx.doi.org/10.1007/bf00962821.

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41

Goutas, S., P. Soupos, and D. Christodoulakis. "Formalization of object-oriented database model with rules." Information and Software Technology 33, no. 10 (December 1991): 741–57. http://dx.doi.org/10.1016/0950-5849(91)90048-g.

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42

Ling, T. W., and P. K. Teo. "On rules and integrity constraints in database systems." Information and Software Technology 34, no. 3 (March 1992): 147–58. http://dx.doi.org/10.1016/0950-5849(92)90026-l.

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43

Qu, Zhi Cheng, Meng Ye, and Bin Jiang. "Mining Method for Weighted Concise Association Rules Based on Closed Itemsets under Weighted Support Framework." Applied Mechanics and Materials 236-237 (November 2012): 326–33. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.326.

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Association rules tell us interesting relationships between different items in transaction database. But traditional association rule has two disadvantages. Firstly it assumes every two items have same significance in database, which is unreasonable in many real applications and usually leads to incorrect results. On the other hand, traditional association rule representation contains too much redundancy which makes it difficult to be mined and used. This paper addresses the problem of mining weighted concise association rules based on closed itemsets under weighted support-significant framework, in which each item with different significance is assigned different weight. Through exploiting specific technique, the proposed algorithm can mine all weighted concise association rules while duplicate weighted itemset search space is pruned. As illustrated in experiments, the proposed method leads to good results and achieves good performance.

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44

Shimada, Kaoru, Kotaro Hirasawa, and Jinglu Hu. "Alternate Genetic Network Programming with Association Rules Acquisition Mechanisms Between Attribute Families." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 6 (November 20, 2006): 954–63. http://dx.doi.org/10.20965/jaciii.2006.p0954.

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A method of association rule mining with chi-squared test using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of node function sets. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. The method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.

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45

Abarajithan, Sivamathi, and S. Vijayarani Mohan. "Cockroach Swarm Optimization Algorithm for High Utility Association Rule Mining." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 58–77. http://dx.doi.org/10.4018/ijsir.2021070103.

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Association rule mining is an important and widely used data mining technique. It is used to retrieve highly related objects in a database based on the occurrence. Recently, utility-based association rules were proposed to consider significant factors of the object. The main objective of this research work is to retrieve high utility association rules from a database using cockroach swarm optimization algorithm. So far, in the literature, no optimization algorithm was proposed in utility-based association rule mining. In this research work, CSOUAR (cockroach swarm optimization for high utility association rule mining) algorithm was proposed to generate utility association rules. CSOUAR algorithm is based on three behaviours of cockroach: chase-swarming, dispersing, and ruthless. To analyse the performance of CSOUAR, an improved particle swarm optimization (PSO-UAR), animal migration optimization (AMO-UAR), bees swarm optimisation (BSO-UAR), and penguins search optimisation (peSO-UAR) are also proposed in this work.

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46

Kolaitis, Phokion G., Lucian Popa, and Kun Qian. "Knowledge Refinement via Rule Selection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2886–94. http://dx.doi.org/10.1609/aaai.v33i01.33012886.

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In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size.

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47

BOSTAN-KORPEOGLU, BURCIN, and ADNAN YAZICI. "INCORPORATING FUZZINESS INTO ACTIVE RULES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 05 (October 2008): 735–57. http://dx.doi.org/10.1142/s0218488508005595.

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Knowledge intensive applications require an intelligent environment, which can perform deductions in response to user queries or events that occur inside or outside of the applications. For that, we propose a fuzzy active object-oriented database for modeling knowledge intensive applications. In that, we incorporate fuzziness within the event, condition and action parts of an active rule. We consider deductive rules as special cases of active rules so that deductive queries are handled using abstract kind of events. We also introduce a model for fuzzy inferencing of fuzzy active rules where we develop a model for scenario concept. We use a Fuzzy Petri Net model for fuzzy rule-based inference.

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48

Watanabe, Toshihiko. "An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (November 20, 2011): 1248–55. http://dx.doi.org/10.20965/jaciii.2011.p1248.

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In data mining approach, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the redundancy of fuzzy association rules as a new concept for mining and prove essential theorems concerning the redundancy of fuzzy association rules. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the redundancy of the extracted rules. The essential performance of the algorithmis evaluated through numerical experiments using benchmark data. Fromthe results, themethod is found to be promising in terms of computational time and redundant-rule pruning.

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49

Tan, Jun. "Different Types of Association Rules Mining Review." Applied Mechanics and Materials 241-244 (December 2012): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1589.

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Анотація:

In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

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50

Do Van, Thanh, and Phuong Truong Duc. "FUZZY COMMON SEQUENTIAL RULES MINING IN QUANTITATIVE SEQUENCE DATABASES." Journal of Computer Science and Cybernetics 35, no. 3 (August 15, 2019): 217–32. http://dx.doi.org/10.15625/1813-9663/0/0/13277.

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Common Sequential Rules present a relationship between unordered itemsets in which the items in antecedents have to appear before ones in consequents. The algorithms proposed to find the such rules so far are only applied for transactional sequence databases, not applied for quantitative sequence databases.The goal of this paper is to propose a new algorithm for finding the fuzzy common sequential (FCS for short) rules in quantitative sequence databases. The proposed algorithm is improved by basing on the ERMiner algorithm. It is considered to be the most effective today compared to other algorithms for finding common sequential rules in transactional sequence database. FCS rules are more general than classical fuzzy sequential rules and are useful in marketing, market analysis, medical diagnosis and treatment

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