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Academic literature on the topic 'Mining Frequent Patterns'

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Journal articles on the topic "Mining Frequent Patterns"

1

Han, Jiawei, and Jian Pei. "Mining frequent patterns by pattern-growth." ACM SIGKDD Explorations Newsletter 2, no. 2 (December 2000): 14–20. http://dx.doi.org/10.1145/380995.381002.

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2

S., Sivaranjani. "Detecting Congestion Patterns in Spatio Temporal Traffic Data Using Frequent Pattern Mining." Bonfring International Journal of Networking Technologies and Applications 5, no. 1 (March 30, 2018): 21–23. http://dx.doi.org/10.9756/bijnta.8372.

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3

KRIBII, Rajae, and Youssef FAKIR. "Mining Frequent Sequential Patterns." Journal of Big Data Research 1, no. 2 (March 15, 2021): 20–37. http://dx.doi.org/10.14302/issn.2768-0207.jbr-21-3455.

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In recent times, the urge to collect data and analyze it has grown. Time stamping a data set is an important part of the analysis and data mining as it can give information that is more useful. Different mining techniques have been designed for mining time-series data, sequential patterns for example seeks relationships between occurrences of sequential events and finds if there exist any specific order of the occurrences. Many Algorithms has been proposed to study this data type based on the apriori approach. In this paper we compare two basic sequential algorithms which are General Sequential algorithm (GSP) and Sequential PAttern Discovery using Equivalence classes (SPADE). These two algorithms are based on the Apriori algorithms. Experimental results have shown that SPADE consumes less time than GSP algorithm.
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Huang, Hao, Xindong Wu, and Richard Relue. "Mining frequent patterns with the pattern tree." New Generation Computing 23, no. 4 (December 2005): 315–37. http://dx.doi.org/10.1007/bf03037636.

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Abdelaal, Areej Ahmad, Sa'ed Abed, Mohammad Al-Shayeji, and Mohammad Allaho. "Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining." Expert Systems with Applications 169 (May 2021): 114530. http://dx.doi.org/10.1016/j.eswa.2020.114530.

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Ott, Jurg, and Taesung Park. "Overview of frequent pattern mining." Genomics & Informatics 20, no. 4 (December 31, 2022): e39. http://dx.doi.org/10.5808/gi.22074.

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Various methods of frequent pattern mining have been applied to genetic problems, specifically, to the combined association of two genotypes (a genotype pattern, or diplotype) at different DNA variants with disease. These methods have the ability to come up with a selection of genotype patterns that are more common in affected than unaffected individuals, and the assessment of statistical significance for these selected patterns poses some unique problems, which are briefly outlined here.
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Yun, Unil, and Eunchul Yoon. "An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 06 (December 2014): 879–912. http://dx.doi.org/10.1142/s0218488514500470.

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Based on the frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining have been studied to reduce the search space and discover important patterns. In the previous definition of weighted closed patterns, supports of patterns are only considered to compute the closures of the patterns. It means that the closures of weighted frequent patterns cannot be perfectly checked. Moreover, the usefulness of weighted closed frequent patterns depends on the presence of frequent patterns that have supersets with the exactly same weighted support. However, from the errors such as noise, slight changes in items' supports or weights by them have significantly negative effects on the mining results, which may prevent us from obtaining exact and valid analysis results since the errors can break the original characteristics of items and patterns. In this paper, to solve the above problems, we propose a concept of robust weighted closed frequent pattern mining, and an approximate bound is defined on the basis of the concept, which can relax requirements for precise equality among patterns' weighted supports. Thereafter, we propose a weighted approximate closed frequent pattern mining algorithm which not only considers the two approaches but also suggests fault tolerant pattern mining in the noise constraints. To efficiently mine weighted approximate closed frequent patterns, we suggest pruning and subset checking methods which reduce search space. We also report extensive performance study to demonstrate the effectiveness, efficiency, memory usage, scalability, and quality of patterns in our algorithm.
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Santoro, Diego, Andrea Tonon, and Fabio Vandin. "Mining Sequential Patterns with VC-Dimension and Rademacher Complexity." Algorithms 13, no. 5 (May 18, 2020): 123. http://dx.doi.org/10.3390/a13050123.

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Sequential pattern mining is a fundamental data mining task with application in several domains. We study two variants of this task—the first is the extraction of frequent sequential patterns, whose frequency in a dataset of sequential transactions is higher than a user-provided threshold; the second is the mining of true frequent sequential patterns, which appear with probability above a user-defined threshold in transactions drawn from the generative process underlying the data. We present the first sampling-based algorithm to mine, with high confidence, a rigorous approximation of the frequent sequential patterns from massive datasets. We also present the first algorithms to mine approximations of the true frequent sequential patterns with rigorous guarantees on the quality of the output. Our algorithms are based on novel applications of Vapnik-Chervonenkis dimension and Rademacher complexity, advanced tools from statistical learning theory, to sequential pattern mining. Our extensive experimental evaluation shows that our algorithms provide high-quality approximations for both problems we consider.
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Aida Jusoh, Julaily, Mustafa Man, and Wan Aezwani Wan Abu Bakar. "Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset." International Journal of Engineering & Technology 7, no. 4.1 (September 12, 2018): 134. http://dx.doi.org/10.14419/ijet.v7i4.1.28241.

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Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time.
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Xue, Linyan, Xiaoke Zhang, Fei Xie, Shuang Liu, and Peng Lin. "Frequent Patterns Algorithm of Biological Sequences based on Pattern Prefix-tree." International Journal of Computers Communications & Control 14, no. 4 (August 5, 2019): 574–89. http://dx.doi.org/10.15837/ijccc.2019.4.3607.

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In the application of bioinformatics, the existing algorithms cannot be directly and efficiently implement sequence pattern mining. Two fast and efficient biological sequence pattern mining algorithms for biological single sequence and multiple sequences are proposed in this paper. The concept of the basic pattern is proposed, and on the basis of mining frequent basic patterns, the frequent pattern is excavated by constructing prefix trees for frequent basic patterns. The proposed algorithms implement rapid mining of frequent patterns of biological sequences based on pattern prefix trees. In experiment the family sequence data in the pfam protein database is used to verify the performance of the proposed algorithm. The prediction results confirm that the proposed algorithms can’t only obtain the mining results with effective biological significance, but also improve the running time efficiency of the biological sequence pattern mining.
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Dissertations / Theses on the topic "Mining Frequent Patterns"

1

Soztutar, Enis. "Mining Frequent Semantic Event Patterns." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611007/index.pdf.

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Especially with the wide use of dynamic page generation, and richer user interaction in Web, traditional web usage mining methods, which are based on the pageview concept are of limited usability. For overcoming the difficulty of capturing usage behaviour, we define the concept of semantic events. Conceptually, events are higher level actions of a user in a web site, that are technically independent of pageviews. Events are modelled as objects in the domain of the web site, with associated properties. A sample event from a video web site is the '
play video event'
with properties '
video'
, '
length of video'
, '
name of video'
, etc. When the event objects belong to the domain model of the web site'
s ontology, they are referred as semantic events. In this work, we propose a new algorithm and associated framework for mining patterns of semantic events from the usage logs. We present a method for tracking and logging domain-level events of a web site, adding semantic information to events, an ordering of events in respect to the genericity of the event, and an algorithm for computing sequences of frequent events.
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2

Jin, Ruoming. "New techniques for efficiently discovering frequent patterns." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1121795612.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xvii, 170 p.; also includes graphics. Includes bibliographical references (p. 160-170). Available online via OhioLINK's ETD Center
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3

Zhang, Qi. "The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261.

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4

Bifet, Albert. "Adaptive Learning and Mining for Data Streams and Frequent Patterns." Doctoral thesis, Universitat Politècnica de Catalunya, 2009. http://hdl.handle.net/10803/22738.

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Aquesta tesi està dedicada al disseny d'algorismes de mineria de dades per fluxos de dades que evolucionen en el temps i per l'extracció d'arbres freqüents tancats. Primer ens ocupem de cadascuna d'aquestes tasques per separat i, a continuació, ens ocupem d'elles conjuntament, desenvolupant mètodes de classificació de fluxos de dades que contenen elements que són arbres. En el model de flux de dades, les dades arriben a gran velocitat, i els algorismes que els han de processar tenen limitacions estrictes de temps i espai. En la primera part d'aquesta tesi proposem i mostrem un marc per desenvolupar algorismes que aprenen de forma adaptativa dels fluxos de dades que canvien en el temps. Els nostres mètodes es basen en l'ús de mòduls detectors de canvi i estimadors en els llocs correctes. Proposem ADWIN, un algorisme de finestra lliscant adaptativa, per la detecció de canvi i manteniment d'estadístiques actualitzades, i proposem utilitzar-lo com a caixa negra substituint els comptadors en algorismes inicialment no dissenyats per a dades que varien en el temps. Com ADWIN té garanties teòriques de funcionament, això obre la possibilitat d'ampliar aquestes garanties als algorismes d'aprenentatge i de mineria de dades que l'usin. Provem la nostre metodologia amb diversos mètodes d'aprenentatge com el Naïve Bayes, partició, arbres de decisió i conjunt de classificadors. Construïm un marc experimental per fer mineria amb fluxos de dades que varien en el temps, basat en el programari MOA, similar al programari WEKA, de manera que sigui fàcil pels investigadors de realitzar-hi proves experimentals. Els arbres són grafs acíclics connectats i són estudiats com vincles en molts casos. En la segona part d'aquesta tesi, descrivim un estudi formal dels arbres des del punt de vista de mineria de dades basada en tancats. A més, presentem algorismes eficients per fer tests de subarbres i per fer mineria d'arbres freqüents tancats ordenats i no ordenats. S'inclou una anàlisi de l'extracció de regles d'associació de confiança plena dels conjunts d'arbres tancats, on hem trobat un fenomen interessant: les regles que la seva contrapart proposicional és no trivial, són sempre certes en els arbres a causa de la seva peculiar combinatòria. I finalment, usant aquests resultats en fluxos de dades evolutius i la mineria d'arbres tancats freqüents, hem presentat algorismes d'alt rendiment per fer mineria d'arbres freqüents tancats de manera adaptativa en fluxos de dades que evolucionen en el temps. Introduïm una metodologia general per identificar patrons tancats en un flux de dades, utilitzant la Teoria de Reticles de Galois. Usant aquesta metodologia, desenvolupem un algorisme incremental, un basat en finestra lliscant, i finalment un que troba arbres freqüents tancats de manera adaptativa en fluxos de dades. Finalment usem aquests mètodes per a desenvolupar mètodes de classificació per a fluxos de dades d'arbres.
This thesis is devoted to the design of data mining algorithms for evolving data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algorithms that must process them have very strict constraints of space and time. In the first part of this thesis we propose and illustrate a framework for developing algorithms that can adaptively learn from data streams that change over time. Our methods are based on using change detectors and estimator modules at the right places. We propose an adaptive sliding window algorithm ADWIN for detecting change and keeping updated statistics from a data stream, and use it as a black-box in place or counters or accumulators in algorithms initially not designed for drifting data. Since ADWIN has rigorous performance guarantees, this opens the possibility of extending such guarantees to learning and mining algorithms. We test our methodology with several learning methods as Naïve Bayes, clustering, decision trees and ensemble methods. We build an experimental framework for data stream mining with concept drift, based on the MOA framework, similar to WEKA, so that it will be easy for researchers to run experimental data stream benchmarks. Trees are connected acyclic graphs and they are studied as link-based structures in many cases. In the second part of this thesis, we describe a rather formal study of trees from the point of view of closure-based mining. Moreover, we present efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. We include an analysis of the extraction of association rules of full confidence out of the closed sets of trees, and we have found there an interesting phenomenon: rules whose propositional counterpart is nontrivial are, however, always implicitly true in trees due to the peculiar combinatorics of the structures. And finally, using these results on evolving data streams mining and closed frequent tree mining, we present high performance algorithms for mining closed unlabeled rooted trees adaptively from data streams that change over time. We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop an incremental one, a sliding-window based one, and finally one that mines closed trees adaptively from data streams. We use these methods to develop classification methods for tree data streams.
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5

Bifet, Figuerol Albert Carles. "Adaptive Learning and Mining for Data Streams and Frequent Patterns." Doctoral thesis, Universitat Politècnica de Catalunya, 2009. http://hdl.handle.net/10803/22738.

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Aquesta tesi està dedicada al disseny d'algorismes de mineria de dades per fluxos de dades que evolucionen en el temps i per l'extracció d'arbres freqüents tancats. Primer ens ocupem de cadascuna d'aquestes tasques per separat i, a continuació, ens ocupem d'elles conjuntament, desenvolupant mètodes de classificació de fluxos de dades que contenen elements que són arbres. En el model de flux de dades, les dades arriben a gran velocitat, i els algorismes que els han de processar tenen limitacions estrictes de temps i espai. En la primera part d'aquesta tesi proposem i mostrem un marc per desenvolupar algorismes que aprenen de forma adaptativa dels fluxos de dades que canvien en el temps. Els nostres mètodes es basen en l'ús de mòduls detectors de canvi i estimadors en els llocs correctes. Proposem ADWIN, un algorisme de finestra lliscant adaptativa, per la detecció de canvi i manteniment d'estadístiques actualitzades, i proposem utilitzar-lo com a caixa negra substituint els comptadors en algorismes inicialment no dissenyats per a dades que varien en el temps. Com ADWIN té garanties teòriques de funcionament, això obre la possibilitat d'ampliar aquestes garanties als algorismes d'aprenentatge i de mineria de dades que l'usin. Provem la nostre metodologia amb diversos mètodes d'aprenentatge com el Naïve Bayes, partició, arbres de decisió i conjunt de classificadors. Construïm un marc experimental per fer mineria amb fluxos de dades que varien en el temps, basat en el programari MOA, similar al programari WEKA, de manera que sigui fàcil pels investigadors de realitzar-hi proves experimentals. Els arbres són grafs acíclics connectats i són estudiats com vincles en molts casos. En la segona part d'aquesta tesi, descrivim un estudi formal dels arbres des del punt de vista de mineria de dades basada en tancats. A més, presentem algorismes eficients per fer tests de subarbres i per fer mineria d'arbres freqüents tancats ordenats i no ordenats. S'inclou una anàlisi de l'extracció de regles d'associació de confiança plena dels conjunts d'arbres tancats, on hem trobat un fenomen interessant: les regles que la seva contrapart proposicional és no trivial, són sempre certes en els arbres a causa de la seva peculiar combinatòria. I finalment, usant aquests resultats en fluxos de dades evolutius i la mineria d'arbres tancats freqüents, hem presentat algorismes d'alt rendiment per fer mineria d'arbres freqüents tancats de manera adaptativa en fluxos de dades que evolucionen en el temps. Introduïm una metodologia general per identificar patrons tancats en un flux de dades, utilitzant la Teoria de Reticles de Galois. Usant aquesta metodologia, desenvolupem un algorisme incremental, un basat en finestra lliscant, i finalment un que troba arbres freqüents tancats de manera adaptativa en fluxos de dades. Finalment usem aquests mètodes per a desenvolupar mètodes de classificació per a fluxos de dades d'arbres.
This thesis is devoted to the design of data mining algorithms for evolving data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees. In the data stream model, data arrive at high speed, and the algorithms that must process them have very strict constraints of space and time. In the first part of this thesis we propose and illustrate a framework for developing algorithms that can adaptively learn from data streams that change over time. Our methods are based on using change detectors and estimator modules at the right places. We propose an adaptive sliding window algorithm ADWIN for detecting change and keeping updated statistics from a data stream, and use it as a black-box in place or counters or accumulators in algorithms initially not designed for drifting data. Since ADWIN has rigorous performance guarantees, this opens the possibility of extending such guarantees to learning and mining algorithms. We test our methodology with several learning methods as Naïve Bayes, clustering, decision trees and ensemble methods. We build an experimental framework for data stream mining with concept drift, based on the MOA framework, similar to WEKA, so that it will be easy for researchers to run experimental data stream benchmarks. Trees are connected acyclic graphs and they are studied as link-based structures in many cases. In the second part of this thesis, we describe a rather formal study of trees from the point of view of closure-based mining. Moreover, we present efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. We include an analysis of the extraction of association rules of full confidence out of the closed sets of trees, and we have found there an interesting phenomenon: rules whose propositional counterpart is nontrivial are, however, always implicitly true in trees due to the peculiar combinatorics of the structures. And finally, using these results on evolving data streams mining and closed frequent tree mining, we present high performance algorithms for mining closed unlabeled rooted trees adaptively from data streams that change over time. We introduce a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop an incremental one, a sliding-window based one, and finally one that mines closed trees adaptively from data streams. We use these methods to develop classification methods for tree data streams.
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Seyfi, Majid. "Mining discriminative itemsets in data streams using different window models." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/120850/1/Majid_Seyfi_Thesis.pdf.

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Big data availability in areas such as social networks, online marketing systems and stock markets is a good source for knowledge discovery. This thesis studies how discriminative itemsets can be discovered in the data streams made of transactions out of user profiles. Discriminative itemsets are frequent in one data stream with much higher frequencies than same itemsets in other data streams in the application domain. This research uses heuristics to manage the large and complex datasets by decreasing the number of candidate patterns. This gives researchers a better understanding of pattern mining in multiple data streams.
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El-Sayed, Maged F. "An efficient and incremental system to mine contiguous frequent sequences." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0130104-115506.

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Meng, Jinghan. "Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6900.

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In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-graph as an effective model to represent information and its related graph mining techniques. In frequent pattern mining in a single-graph setting, there are two main problems: support measure and search scheme. In this paper, we propose a novel framework for constructing support measures that brings together existing minimum-image-based and overlap-graph-based support measures. Our framework is built on the concept of occurrence / instance hypergraphs. Based on that, we present two new support measures: minimum instance (MI) measure and minimum vertex cover (MVC) measure, that combine the advantages of existing measures. In particular, we show that the existing minimum-image-based support measure is an upper bound of the MI measure, which is also linear-time computable and results in counts that are close to number of instances of a pattern. Although the MVC measure is NP-hard, it can be approximated to a constant factor in polynomial time. We also provide polynomial-time relaxations for both measures and bounding theorems for all presented support measures in the hypergraph setting. We further show that the hypergraph-based framework can unify all support measures studied in this paper. This framework is also flexible in that more variants of support measures can be defined and profiled in it.
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Kilic, Sefa. "Clustering Frequent Navigation Patterns From Website Logs Using Ontology And Temporal Information." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12613979/index.pdf.

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Given set of web pages labeled with ontological items, the level of similarity between two web pages is measured using the level of similarity between ontological items of pages labeled with. Using similarity measure between two pages, degree of similarity between two sequences of web page visits can be calculated as well. Using clustering algorithms, similar frequent sequences are grouped and representative sequences are selected from these groups. A new sequence is compared with all clusters and it is assigned to most similar one. Representatives of the most similar cluster can be used in several real world cases. They can be used for predicting and prefetching the next page user will visit or for helping the navigation of user in the website. They can also be used to improve the structure of website for easier navigation. In this study the effect of time spent on each web page during the session is analyzed.
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TATAVARTY, GIRIDHAR. "FINDING TEMPORAL ASSOCIATION RULES BETWEEN FREQUENT PATTERNS IN MULTIVARIATE TIME SERIES." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141325950.

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Books on the topic "Mining Frequent Patterns"

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Aggarwal, Charu C., and Jiawei Han, eds. Frequent Pattern Mining. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2.

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Aydin, Berkay, and Rafal A. Angryk. Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99873-2.

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Indian Institute of Management, Ahmedabad., ed. An efficient algorithm for frequent pattern mining for real-time business intelligence analytics in dense datasets. Ahmedabad: Indian Institute of Management, 2005.

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Aggarwal, Charu C., and Jiawei Han. Frequent Pattern Mining. Springer London, Limited, 2014.

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Aggarwal, Charu C., and Jiawei Han. Frequent Pattern Mining. Springer, 2016.

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Frequent Pattern Mining. Springer, 2014.

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“Data Mining Concepts & Techniques”. 3rd ed. Morgan Kaufmann Publishers, 2011.

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Aydin, Berkay, and Rafal A. Angryk. Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories. Springer, 2018.

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Book chapters on the topic "Mining Frequent Patterns"

1

Vreeken, Jilles, and Nikolaj Tatti. "Interesting Patterns." In Frequent Pattern Mining, 105–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_5.

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Wu, Di. "Frequent Patterns." In Data Mining with Python, 356–69. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003462781-9.

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Cheng, Hong, Xifeng Yan, and Jiawei Han. "Mining Graph Patterns." In Frequent Pattern Mining, 307–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_13.

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Zhu, Feida. "Mining Long Patterns." In Frequent Pattern Mining, 83–104. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_4.

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van Leeuwen, Matthijs, and Jilles Vreeken. "Mining and Using Sets of Patterns through Compression." In Frequent Pattern Mining, 165–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_8.

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Deng, Zhi-Hong, Cong-Rui Ji, Ming Zhang, and Shi-Wei Tang. "Mining Frequent Ordered Patterns." In Advances in Knowledge Discovery and Data Mining, 150–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_19.

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Tseng, Fan-Chen, and Ching-Chi Hsu. "Generating Frequent Patterns with the Frequent Pattern List." In Advances in Knowledge Discovery and Data Mining, 376–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45357-1_40.

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Nofong, Vincent Mwintieru, Hamidu Abdel-Fatao, Michael Kofi Afriyie, and John Wondoh. "Discovering Self-reliant Periodic Frequent Patterns." In Periodic Pattern Mining, 105–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_7.

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Lonlac, Jerry, Arnaud Doniec, Marin Lujak, and Stephane Lecoeuche. "Mining Frequent Seasonal Gradual Patterns." In Big Data Analytics and Knowledge Discovery, 197–207. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59065-9_16.

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Ahmed, Usman, Jerry Chun-Wei Lin, and Philippe Fournier-Viger. "Privacy Preservation of Periodic Frequent Patterns Using Sensitive Inverse Frequency." In Periodic Pattern Mining, 215–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_12.

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Conference papers on the topic "Mining Frequent Patterns"

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Qunxiong Zhu and Xiaoyong Lin. "Mining Frequent Patterns with Incremental Updating Frequent Pattern Tree." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1714215.

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Zhou, Zhongmei. "Mining Frequent Independent Patterns and Frequent Correlated Patterns Synchronously." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.27.

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Junyan Zhang and Fan Min. "Mining frequent patterns from sequences." In 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6183913.

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Zhang, Junyan, and Fan Min. "Mining Frequent Patterns From Sequences." In 2013 2nd International Conference on Intelligent System and Applied Material. Ottawa: EDUGAIT Press, 2013. http://dx.doi.org/10.12696/gsam.2013.0830.

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Meng, Hui, Lifa Wu, Tianlei Zhang, Guisheng Chen, and Deyi Li. "Mining Frequent Composite Service Patterns." In 2008 Seventh International Conference on Grid and Cooperative Computing (GCC). IEEE, 2008. http://dx.doi.org/10.1109/gcc.2008.102.

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Zhu, Feida, Xifeng Yan, Jiawei Han, Philip S. Yu, and Hong Cheng. "Mining Colossal Frequent Patterns by Core Pattern Fusion." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.367916.

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Bashir, Shariq, Zahid Halim, and A. Rauf Baig. "Mining fault tolerant frequent patterns using pattern growth approach." In 2008 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2008. http://dx.doi.org/10.1109/aiccsa.2008.4493532.

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Yildiz, Baris, and Hatice Selale. "Mining frequent patterns from microarray data." In 2011 6th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2011. http://dx.doi.org/10.1109/hibit.2011.6450819.

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Yen, Show-Jane, Yue-Shi Lee, Yu-Ting Guo, and Jia-Yuan Gu. "Mining frequent patterns from incremental databases." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016712.

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Deng, Zhi-Hong, and Guo-Dong Fang. "Mining Top-Rank-K Frequent Patterns." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370261.

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Reports on the topic "Mining Frequent Patterns"

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Shekhar, Shashi, Pradeep Mohan, Dev Oliver, and Xun Zhou. Crime Pattern Analysis: A Spatial Frequent Pattern Mining Approach. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada561517.

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