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Journal articles on the topic 'Mining pattern'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

S, Abirami. "Pattern-Growth Methods for Frequent Pattern Mining." Shanlax International Journal of Arts, Science and Humanities 6, S1 (2018): 76–81. https://doi.org/10.5281/zenodo.1410989.

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Mining frequent patterns from large databases play an essential role in many data mining tasks and has broad applications. Most of the previously proposed methods adopt Apriori-like candidate-generation-and-test approaches. However, those methods  may  encounter  serious  challenges  when  mining  datasets  with  prolific patterns and long patterns.In this work, to develop a class of novel and efficient pattern-growth methods for mining various frequent patterns from large databases. Pattern-growth methods  adopt a divi

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Yun, Unil, Gwangbum Pyun, and Eunchul Yoon. "Efficient Mining of Robust Closed Weighted Sequential Patterns Without Information Loss." International Journal on Artificial Intelligence Tools 24, no. 01 (2015): 1550007. http://dx.doi.org/10.1142/s0218213015500074.

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Sequential pattern mining has become one of the most important topics in data mining. It has broad applications such as analyzing customer purchase data, Web access patterns, network traffic data, DNA sequencing, and so on. Previous studies have concentrated on reducing redundant patterns among the sequential patterns, and on finding meaningful patterns from huge datasets. In sequential pattern mining, closed sequential pattern mining and weighted sequential pattern mining are the two main approaches to perform mining tasks. This is because closed sequential pattern mining finds representative

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

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Chai, Xin, Dan Yang, Jingyu Liu, Yan Li, and Youxi Wu. "Top-k sequence pattern mining with non-overlapping condition." Filomat 32, no. 5 (2018): 1703–10. http://dx.doi.org/10.2298/fil1805703c.

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Pattern mining has been widely applied in many fields. Users often mine a large number of patterns. However, most of these are difficult to apply in real applications. Top-k pattern mining, which involves finding the most frequent k patterns, is an effective strategy, because the more frequently a pattern occurs, the more likely they are to be important for users. However, top-k mining can only mine short patterns in mining applications with the Apriori property. It is well-known that short patterns contain less information than long patterns. In this paper, we focus on mining top-k sequence p

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Harco, Leslie Hendric Spits Warnars, Trisetyarso Agung, and Randriatoamanana Richard. "Confidence of AOI-HEP Mining Pattern." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1217–25. https://doi.org/10.12928/TELKOMNIKA.v16i3.5303.

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Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) has been proven can mine frequent and similar patterns and the finding AOI-HEP patterns will be underlined with confidence mining pattern for each AOI-HEP pattern either frequent or similar pattern, and each dataset as confidence AOIHEP pattern between frequent and similar patterns. Confidence per AOI-HEP pattern will show how interested each of AOI-HEP pattern, whilst confidende per dataset will show how interested each dataset between frequent and similar patterns. The experiments for finding confidence of each AOI-HEP patter

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Han, Jia-Wei, Jian Pei, and Xi-Feng Yan. "From sequential pattern mining to structured pattern mining: A pattern-growth approach." Journal of Computer Science and Technology 19, no. 3 (2004): 257–79. http://dx.doi.org/10.1007/bf02944897.

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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 (2018): 21–23. http://dx.doi.org/10.9756/bijnta.8372.

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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 (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 knowledg

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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 (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 e

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Patil, Kirti S., and Sandip S. Patil. "Sequential Pattern Mining Using Algorithm." Asian Journal of Computer Science and Technology 2, no. 1 (2013): 19–21. http://dx.doi.org/10.51983/ajcst-2013.2.1.1715.

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The concept of Sequential Pattern Mining was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in the year 1995. Sequential Patterns are used to discover sequential sub-sequences among large amount of sequential data. In web usage mining, sequential patterns are exploited to find sequential navigation patterns that appear in users’ sessions sequentially. The information obtained from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. In this paper, a new algorithm is proposed; it combines the Apriori algorithm and FP-tree structure which p

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Ma, Qian, and Jingfeng Guo. "Mining Multi-Patterns in Pattern-Based Clustering." Procedia Engineering 29 (2012): 3179–83. http://dx.doi.org/10.1016/j.proeng.2012.01.462.

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Huang, Hao, Xindong Wu, and Richard Relue. "Mining frequent patterns with the pattern tree." New Generation Computing 23, no. 4 (2005): 315–37. http://dx.doi.org/10.1007/bf03037636.

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Tran, Duong Huy, Thang Truong Nguyen, Thi Duc Vu, and Anh The Tran. "MINING TOP-K FREQUENT SEQUENTIAL PATTERN IN ITEM INTERVAL EXTENDED SEQUENCE DATABASE." Journal of Computer Science and Cybernetics 34, no. 3 (2018): 249–63. http://dx.doi.org/10.15625/1813-9663/34/3/13053.

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Abstract. Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will

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J., Umarani, and S. Manikandan Dr. "PATTERN DISCOVERY TECHNIQUES IN WEB USAGE MINING." International Journal of Scientific Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332044.

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WWW is a very popular and interactive medium for broadcasting information today. Due to the vast, diverse and lively nature of web it advancesthe scalability, multimedia data and temporal issues respectively. The development of the web has given rise to large quantity of data that is freely available for user access.Web Usage Mining enhances the user experience while browsing web pages by using past history of web data. It also used to improve the web site navigation. Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web usage mining is

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Neubarth, Kerstin, and Darrell Conklin. "Mining Characteristic Patterns for Comparative Music Corpus Analysis." Applied Sciences 10, no. 6 (2020): 1991. http://dx.doi.org/10.3390/app10061991.

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A core issue of computational pattern mining is the identification of interesting patterns. When mining music corpora organized into classes of songs, patterns may be of interest because they are characteristic, describing prevalent properties of classes, or because they are discriminant, capturing distinctive properties of classes. Existing work in computational music corpus analysis has focused on discovering discriminant patterns. This paper studies characteristic patterns, investigating the behavior of different pattern interestingness measures in balancing coverage and discriminability of

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Ott, Jurg, and Taesung Park. "Overview of frequent pattern mining." Genomics & Informatics 20, no. 4 (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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Ziembiński, Radosław. "Algorithms for Context Based Sequential Pattern Mining." Fundamenta Informaticae 76, no. 4 (2007): 495–510. https://doi.org/10.3233/fun-2007-76405.

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This paper describes practical aspects of a novel approach to the sequential pattern mining named Context Based Sequential Pattern Mining (CBSPM). It introduces a novel ContextMapping algorithm used for the context pattern mining and an illustrative example showing some advantages of the proposed method. The approach presented here takes into consideration some shortcomings of the classic problem of the sequential pattern mining. The significant advantage of the classic sequential patterns mining is simplicity. It introduces simple element construction, built upon set of atomic items. The comp

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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 (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

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Lee, Jung-Hun, and Youn-A. Min. "PPFP(Push and Pop Frequent Pattern Mining): A Novel Frequent Pattern Mining Method for Bigdata Frequent Pattern Mining." KIPS Transactions on Software and Data Engineering 5, no. 12 (2016): 623–34. http://dx.doi.org/10.3745/ktsde.2016.5.12.623.

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Yu, Xiangzhan, Zhaoxin Zhang, Haining Yu, Feng Jiang, and Wen Ji. "An Asynchronous Periodic Sequential Pattern Mining Algorithm with Multiple Minimum Item Supports for Ad Hoc Networking." Journal of Sensors 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/461659.

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The original sequential pattern mining model only considers occurrence frequencies of sequential patterns, disregarding their occurrence periodicity. We propose an asynchronous periodic sequential pattern mining model to discover the sequential patterns that not only occur frequently but also appear periodically. For this mining model, we propose a pattern-growth mining algorithm to mine asynchronous periodic sequential patterns with multiple minimum item supports. This algorithm employs a divide-and-conquer strategy to mine asynchronous periodic sequential patterns in a depth-first manner rec

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Kenmogne, Edith Belise. "The Impact of the Pattern-Growth Ordering on the Performances of Pattern Growth-Based Sequential Pattern Mining Algorithms." Computer and Information Science 10, no. 1 (2016): 23. http://dx.doi.org/10.5539/cis.v10n1p23.

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Sequential Pattern Mining is an efficient technique for discovering recurring structures or patterns from very large datasetwidely addressed by the data mining community, with a very large field of applications, such as cross-marketing, DNA analysis, web log analysis,user behavior, sensor data, etc. The sequence pattern mining aims at extractinga set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either verti

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Liu, Z. W., B. Wei, C. L. Kang, and J. W. Jiang. "THE IMPLEMENTATION OF HESITANT FUZZY SPATIAL CO-LOCATION PATTERN MINING ALGORITHM BASED ON PYTHON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 763–67. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-763-2020.

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Abstract. As one of the important research directions in the spatial data mining, spatial co-location pattern mining aimed at finding the spatial features whose the instances are frequent co-locate in neighbouring domain. With the introduction of fuzzy sets into traditional spatial co-location pattern mining, the research on fuzzy spatial co-location pattern mining has been deepened continuously, which extends traditional spatial co-location pattern mining to deal with fuzzy spatial objects and discover their laws of spatial symbiosis. In this paper, the operation principle of a classical join

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Baena-Garcı´a, M., and R. Morales-Bueno. "Mining interestingness measures for string pattern mining." Knowledge-Based Systems 25, no. 1 (2012): 45–50. http://dx.doi.org/10.1016/j.knosys.2011.01.013.

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Makhalova, Tatiana, Sergei O. Kuznetsov, and Amedeo Napoli. "Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets." Data Mining and Knowledge Discovery 36, no. 1 (2021): 108–45. http://dx.doi.org/10.1007/s10618-021-00799-9.

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AbstractPattern mining is well established in data mining research, especially for mining binary datasets. Surprisingly, there is much less work about numerical pattern mining and this research area remains under-explored. In this paper we propose Mint, an efficient MDL-based algorithm for mining numerical datasets. The MDL principle is a robust and reliable framework widely used in pattern mining, and as well in subgroup discovery. In Mint we reuse MDL for discovering useful patterns and returning a set of non-redundant overlapping patterns with well-defined boundaries and covering meaningful

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Makhalova, Tatiana, Sergei O. Kuznetsov, and Amedeo Napoli. "Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets." Data Mining and Knowledge Discovery 36, no. 1 (2021): 108–45. http://dx.doi.org/10.1007/s10618-021-00799-9.

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AbstractPattern mining is well established in data mining research, especially for mining binary datasets. Surprisingly, there is much less work about numerical pattern mining and this research area remains under-explored. In this paper we propose Mint, an efficient MDL-based algorithm for mining numerical datasets. The MDL principle is a robust and reliable framework widely used in pattern mining, and as well in subgroup discovery. In Mint we reuse MDL for discovering useful patterns and returning a set of non-redundant overlapping patterns with well-defined boundaries and covering meaningful

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Wu, Youxi, Lanfang Luo, Yan Li, et al. "NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (2022): 1–21. http://dx.doi.org/10.1145/3480245.

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Nonoverlapping sequential pattern mining is an important type of sequential pattern mining (SPM) with gap constraints, which not only can reveal interesting patterns to users but also can effectively reduce the search space using the Apriori (anti-monotonicity) property. However, the existing algorithms do not focus on attributes of interest to users, meaning that existing methods may discover many frequent patterns that are redundant. To solve this problem, this article proposes a task called nonoverlapping three-way sequential pattern (NTP) mining, where attributes are categorized according

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Poonam Sharma та Gudla Balakrishna. "PreﬁxSpan: Mining Sequential Patterns by Preﬁx-Projected Pattern". International Journal of Computer Science & Engineering Survey 2, № 4 (2011): 111–22. http://dx.doi.org/10.5121/ijcses.2011.2408.

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Van, Trang, Atsuo Yoshitaka, and Bac Le. "Mining web access patterns with super-pattern constraint." Applied Intelligence 48, no. 11 (2018): 3902–14. http://dx.doi.org/10.1007/s10489-018-1182-6.

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Ouyang, Zhiping, Lizhen Wang, and Pingping Wu. "Spatial Co-Location Pattern Discovery from Fuzzy Objects." International Journal on Artificial Intelligence Tools 26, no. 02 (2017): 1750003. http://dx.doi.org/10.1142/s0218213017500038.

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A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern min

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Deng, Wei. "Network Subgraph Pattern Mining." Journal of Physics: Conference Series 1881, no. 3 (2021): 032043. http://dx.doi.org/10.1088/1742-6596/1881/3/032043.

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Bithi, Ashin Ara. "Sequential Pattern Tree Mining." IOSR Journal of Computer Engineering 15, no. 5 (2013): 79–78. http://dx.doi.org/10.9790/0661-1557978.

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Vreeken, Jilles. "Making pattern mining useful." ACM SIGKDD Explorations Newsletter 12, no. 1 (2010): 75–76. http://dx.doi.org/10.1145/1882471.1882483.

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Pei, Jian, and Jiawei Han. "Constrained frequent pattern mining." ACM SIGKDD Explorations Newsletter 4, no. 1 (2002): 31–39. http://dx.doi.org/10.1145/568574.568580.

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Zhou, Wenjun, Hui Xiong, Lian Duan, Keli Xiao, and Robert Mee. "Paradoxical Correlation Pattern Mining." IEEE Transactions on Knowledge and Data Engineering 30, no. 8 (2018): 1561–74. http://dx.doi.org/10.1109/tkde.2018.2791602.

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Koh, Yun Sing, and Sri Devi Ravana. "Unsupervised Rare Pattern Mining." ACM Transactions on Knowledge Discovery from Data 10, no. 4 (2016): 1–29. http://dx.doi.org/10.1145/2898359.

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Witno, Witno, Nining Puspaningsih, and Budi Kuncahyo. "POLA SEBARAN SPASIAL BIOMASSA DI AREAL REVEGETASI BEKAS TAMBANG NIKEL." Jurnal Penelitian Kehutanan BONITA 1, no. 2 (2019): 1. http://dx.doi.org/10.55285/bonita.v1i2.308.

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The aim of this study was to identify the spatial pattern of biomass distribution in the revegetation of the post-mining area in PTVI. The nearest neighbour analysis method by comparing the distance of an individual was used to determine the spatial biomass distribution pattern in the post nickel mining revegetation area of PTVI. The nearest neighbour analysis was used to explain the distribution pattern of locations using a calculation that considers the distance, number of locations and acreage. This analysis produced a final result in the form of an index ranging from 0 until more than 1. I

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Trivedi, Nripesh. "Data mining." International Journal of Scientific Research and Management (IJSRM) 12, no. 03 (2024): 1094. http://dx.doi.org/10.18535/ijsrm/v12i03.ec07.

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Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in ma

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Duan, Jiangli, Wang Lizhen, Xin Hu, and Hongmei Chen. "Mining spatial dynamic co-location patterns." Filomat 32, no. 5 (2018): 1491–97. http://dx.doi.org/10.2298/fil1805491d.

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Spatial co-location pattern mining is an important part of spatial data mining, and its purpose is to discover the coexistence spatial feature sets whose instances are frequently located together in a geographic space. So far, many algorithms of mining spatial co-location pattern and their corresponding expansions have been proposed. However, dynamic co-location patterns have not received attention such as the real meaningful pattern {Ganoderma lucidum new, maple tree dead} means that ?Ganoderma lucidum? grows on the ?maple tree? which was already dead. Therefore, in this paper, we propose the

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Dzyuba, Vladimir, Matthijs van Leeuwen, Siegfried Nijssen, and Luc De Raedt. "Interactive Learning of Pattern Rankings." International Journal on Artificial Intelligence Tools 23, no. 06 (2014): 1460026. http://dx.doi.org/10.1142/s0218213014600264.

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Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts. To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to

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Vairaprakash, Gurusamy*1 and K.Nandhini2. "MINING MAXIMAL PERIODIC PATTERNS IN FEWER LEVELS OF RECURSION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 11 (2017): 155–61. https://doi.org/10.5281/zenodo.1042104.

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To reduce the number of levels of recursion for mining maximal periodic patterns a novel data structure, UpDown Directed Acyclic Graph (UDDAG) is invented. UDDAG allows bidirectional pattern growth along both ends of detected patterns which result in fewer levels of recursion than the traditional. To mine k+1 length patterns it uses log<sub>2</sub>k +1 levels of recursion at best instead of k levels. With UDDAG, at level i recursion, we grow the length of patterns by 2i-1 at most. Thus, a length-k pattern can be detected in log<sub>2</sub>k+1 levels of recursion at minimum. Traditionally perio

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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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Et. al., V. Aruna,. "A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1634–39. http://dx.doi.org/10.17762/turcomat.v12i2.1448.

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In the recent years with the advancement in technology, a lot of information is available in different formats and extracting the knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one of the subset of Web Mining helps in extracting the hidden knowledge presen

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Sharma, Vikrant. "Improving Efficiency of High Utility Sequential Pattern Extraction." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 234–42. http://dx.doi.org/10.17762/msea.v70i1.2304.

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Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Stru

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Wang, Le, Shui Wang, Haiyan Li, and Chunliang Zhou. "Improved Strategy for High-Utility Pattern Mining Algorithm." Mathematical Problems in Engineering 2020 (November 26, 2020): 1–11. http://dx.doi.org/10.1155/2020/1971805.

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High-utility pattern mining is a research hotspot in the field of pattern mining, and one of its main research topics is how to improve the efficiency of the mining algorithm. Based on the study on the state-of-the-art high-utility pattern mining algorithms, this paper proposes an improved strategy that removes noncandidate items from the global header table and local header table as early as possible, thus reducing search space and improving efficiency of the algorithm. The proposed strategy is applied to the algorithm EFIM (EFficient high-utility Itemset Mining). Experimental verification wa

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Patel, Sanjay, and Dr Ketan Kotecha. "Incremental Frequent Pattern Mining using Graph based approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (2005): 731–36. http://dx.doi.org/10.24297/ijct.v4i2c2.4191.

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Extracting useful information from huge amount of data is known as Data Mining. It happens at the intersection of artificial intelligence and statistics. It is also defined as the use of computer algorithms to discover hidden patterns and interesting relationships between items in large datasets. Candidate generation and test, Pattern Growth etc. are the common approaches to find frequent patterns from the database. Incremental mining is a crucial requirement for the industries nowadays. Many tree based approaches have tried to extend the frequent pattern mining as an incremental approach, but

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Ge, Cui Cui, and Xiu Fen Fu. "Mining Closed Weighed Frequent Patterns from a Sliding Window over Data Stream." Advanced Materials Research 756-759 (September 2013): 2606–9. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2606.

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Weighted frequent pattern mining address to discover more important frequent pattern by considering different weights of every item, closed frequent pattern mining can significantly reduce the number of frequent itemset mining and keep sufficient result information. In this paper,we proposed an algorithm DS_CRWF to mine closed weighted frequent pattern over data stream,which is based on sliding window and take basic window as unit of updating,all the closed weighted frequent patterns can be mined through once scan.The experimental results show the feasibility of the algorithm.

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Li, Jinhong, Lizhen Wang, Hongmei Chen, and Zhengbao Sun. "Mining spatial high-average utility co-location patterns from spatial data sets." Intelligent Data Analysis 26, no. 4 (2022): 911–31. http://dx.doi.org/10.3233/ida-215848.

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The spatial co-location pattern refers to a subset of non-empty spatial features whose instances are frequently located together in a spatial neighborhood. Traditional spatial co-location pattern mining is mainly based on the frequency of the pattern, and there is no difference in the importance or value of each spatial feature within the pattern. Although the spatial high utility co-location pattern mining solves this problem, it does not consider the effect of pattern length on the utility. Generally, the utility of the pattern also increases as the length of the pattern increases. Therefore

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Wu, Youxi, Xiaohui Wang, Yan Li, et al. "OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining." ACM Transactions on Management Information Systems 13, no. 3 (2022): 1–23. http://dx.doi.org/10.1145/3476247.

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Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid mining too many useless patterns. However, this method is difficult for users to set a suitable gap without prior knowledge and each character is considered to have the same effects. To tackle these issues, this article addresses a self-adaptive One-off Weak-gap Strong Pattern (OWSP) mining, which has three characteristics. First, it determines the gap constraint adaptively according to the sequence. Second, all characters are divided into two groups: strong and weak characters, and the pattern is composed o

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Djenouri, Youcef, Jerry Chun-Wei Lin, Kjetil Nørvåg, Heri Ramampiaro, and Philip S. Yu. "Exploring Decomposition for Solving Pattern Mining Problems." ACM Transactions on Management Information Systems 12, no. 2 (2021): 1–36. http://dx.doi.org/10.1145/3439771.

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This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strat

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Ho, Van Long, Nguyen Ho, and Torben Bach Pedersen. "Efficient temporal pattern mining in big time series using mutual information." Proceedings of the VLDB Endowment 15, no. 3 (2021): 673–85. http://dx.doi.org/10.14778/3494124.3494147.

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Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights can be gained by mining temporal patterns from these time series. Unlike traditional pattern mining, temporal pattern mining (TPM) adds event time intervals into extracted patterns, making them more expressive at the expense of increased time and space complexities. Existing TPM methods either cannot scale to large datasets, or work only on pre-processed temporal events rather than on time series. This paper presents our Frequent Temporal Pa

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