Journal articles: '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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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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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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Han, Jiawei, Jian Pei, Yiwen Yin, and Runying Mao. "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach." Data Mining and Knowledge Discovery 8, no. 1 (January 2004): 53–87. http://dx.doi.org/10.1023/b:dami.0000005258.31418.83.

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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 (November 23, 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 lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining process

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Ezeife, Christie I., Vignesh Aravindan, and Ritu Chaturvedi. "Mining Integrated Sequential Patterns From Multiple Databases." International Journal of Data Warehousing and Mining 16, no. 1 (January 2020): 1–21. http://dx.doi.org/10.4018/ijdwm.2020010101.

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Existing work on multiple databases (MDBs) sequential pattern mining cannot mine frequent sequences to answer exact and historical queries from MDBs having different table structures. This article proposes the transaction id frequent sequence pattern (TidFSeq) algorithm to handle the difficult problem of mining frequent sequences from diverse MDBs. The TidFSeq algorithm transforms candidate 1-sequences to get transaction subsequences where candidate 1-sequences occurred as (1-sequence, itssubsequenceidlist) tuple or (1-sequence, position id list). Subsequent frequent i-sequences are computed using the counts of the sequence ids in each candidate i-sequence position id list tuples. An extended version of the general sequential pattern (GSP)-like candidate generates and a frequency count approach is used for computing supports of itemset (I-step) and separate (S-step) sequences without repeated database scans but with transaction ids. Generated patterns answer complex queries from MDBs. The TidFSeq algorithm has a faster processing time than existing algorithms.

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Jamdar, Nikhil, and A. Vijayalakshmi. "BIG DATA MINING FOR INTERESTING PATTERNS WITH MAP REDUCE TECHNIQUE." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 191. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19634.

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There are many algorithms available in data mining to search interesting patterns from transactional databases of precise data. Frequent pattern mining is a technique to find the frequently occurred items in data mining. Most of the techniques used to find all the interesting patterns from a collection of precise data, where items occurred in each transaction are certainly known to the system. As well as in many real-time applications, users are interested in a tiny portion of large frequent patterns. So the proposed user constrained mining approach, will help to find frequent patterns in which user is interested. This approach will efficiently find user interested frequent patterns by applying user constraints on the collections of uncertain data. The user can specify their own interest in the form of constraints and uses the Map Reduce model to find uncertain frequent pattern that satisfy the user-specified constraints

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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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Rehman, Saif Ur, Muhammad Altaf Khan, Habib Un Nabi, Shaukat Ali, Noha Alnazzawi, and Shafiullah Khan. "TKIFRPM: A Novel Approach for Topmost-K Identical Frequent Regular Patterns Mining from Incremental Datasets." Applied Sciences 13, no. 1 (January 3, 2023): 654. http://dx.doi.org/10.3390/app13010654.

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The regular frequent pattern mining (RFPM) approaches are aimed to discover the itemsets with significant frequency and regular occurrence behavior in a dataset. However, these approaches mainly suffer from the following two issues: (1) setting the frequency threshold parameter for the discovery of regular frequent patterns technique is not an easy task because of its dependency on the characteristics of a dataset, and (2) RFPM approaches are designed to mine patterns from the static datasets and are not able to mine dynamic datasets. This paper aims to solve these two issues by proposing a novel top-K identical frequent regular patterns mining (TKIFRPM) approach to function on online datasets. The TKIFRPM maintains a novel synopsis data structure with item support index tables (ISI-tables) to keep summarized information about online committed transactions and dataset updates. The mining operation can discover top-K regular frequent patterns from online data stored in the ISI-tables. The TKIFRPM explores the search space in recursive depth-first order and applies a novel progressive node’s sub-tree pruning strategy to rapidly eliminate a complete infrequent sub-tree from the search space. The TKIFRPM is compared with the MTKPP approach, and it found that it outperforms its counterpart in terms of runtime and memory usage to produce designated topmost-K frequent regular pattern mining on the datasets following incremental updates.

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Ganatr, Jeet, Mihir Thakkar, Kunal Shah, and Vaishali Gaikwad. "Mining frequent patterns using customer experience." International Journal of Recent Scientific Research 8, no. 03 (March 28, 2017): 15790–95. http://dx.doi.org/10.24327/ijrsr.2017.0803.0005.

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Shanthi, K. V., J. Akilandeswari, and G. Jothi. "Mining frequent patterns without tree generation." International Journal of Data Mining, Modelling and Management 8, no. 3 (2016): 265. http://dx.doi.org/10.1504/ijdmmm.2016.079062.

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Shanthi, K. V., G. Jothi, and J. Akilandeswari. "Mining frequent patterns without tree generation." International Journal of Data Mining, Modelling and Management 8, no. 3 (2016): 265. http://dx.doi.org/10.1504/ijdmmm.2016.10000330.

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20

Liu, Xuejun. "Mining Frequent Patterns in Data Streams." Journal of Computer Research and Development 42, no. 12 (2005): 2192. http://dx.doi.org/10.1360/crad20051224.

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Bonomi, Luca, and Li Xiong. "Mining frequent patterns with differential privacy." Proceedings of the VLDB Endowment 6, no. 12 (August 28, 2013): 1422–27. http://dx.doi.org/10.14778/2536274.2536329.

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Deng, Na, Xu Chen, Desheng Li, and Caiquan Xiong. "Frequent Patterns Mining in DNA Sequence." IEEE Access 7 (2019): 108400–108410. http://dx.doi.org/10.1109/access.2019.2933044.

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Bastide, Yves, Rafik Taouil, Nicolas Pasquier, Gerd Stumme, and Lotfi Lakhal. "Mining frequent patterns with counting inference." ACM SIGKDD Explorations Newsletter 2, no. 2 (December 2000): 66–75. http://dx.doi.org/10.1145/380995.381017.

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Han, Jiawei, Jian Pei, and Yiwen Yin. "Mining frequent patterns without candidate generation." ACM SIGMOD Record 29, no. 2 (June 2000): 1–12. http://dx.doi.org/10.1145/335191.335372.

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Chapela-Campa, David, Manuel Mucientes, and Manuel Lama. "Mining frequent patterns in process models." Information Sciences 472 (January 2019): 235–57. http://dx.doi.org/10.1016/j.ins.2018.09.011.

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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 (November 30, 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 most of the research was limited to interactive mining only. Here, instead of tree based approach, graph based approach is presented which also gives good results for incremental mining. Â

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KEMPE, STEFFEN, JOCHEN HIPP, CARSTEN LANQUILLON, and RUDOLF KRUSE. "MINING FREQUENT TEMPORAL PATTERNS IN INTERVAL SEQUENCES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 05 (October 2008): 645–61. http://dx.doi.org/10.1142/s0218488508005546.

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Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that, at the same time, defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe FSMSet, an algorithm that employs the new support definition, and demonstrate its performance on field data from the automotive business.

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Li, Aiguo, Jiahao Fu, Ruifang Gao, and Jie Yang. "Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint." Scientific Programming 2022 (July 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/6286700.

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In the security system, transforming a large number of collected target trajectories into semantic trajectories with a less volume and high quality and mining their frequent patterns are helpful to analyze the target behavior patterns, identify hazard sources, and enhance the internal prevention, and control of the security system. Aiming at the limitation of semantic trace frequent pattern mining method defined by precise stay time in practical application scenarios, a fuzzy semantic trace frequent pattern mining method is proposed. Firstly, the membership function of fuzzy stay time is defined, so the stay time of the target at the stay point is fuzzified, and the fuzzy semantic trajectory is obtained. Then, a fuzzy semantic trajectory frequent pattern mining algorithm FST-FPM (fuzzy semantic trajectory frequent pattern mining) is proposed. The FST-FPM algorithm is experimentally verified on the Geolife public dataset and the self-collected RFID positioning dataset. The experimental results show that FST-FPM algorithm can mine frequent patterns of fuzzy semantic trajectories on Geolife dataset and RFID positioning dataset, and the running time is reduced by more than 10% compared with classical PrefixSpan algorithm, PrefixSpan-x algorithm, and LFFT2 algorithm.

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Priya, A. Selva. "Comparative Analysis of Algorithms for Mining Frequent Patterns." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 1288–94. http://dx.doi.org/10.22214/ijraset.2023.53799.

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Abstract: In the computerized world, everything is moving online, and data comes in different shapes and sizes and is collected in different ways. By using data mining, frequent pattern in the databases can be identified, and it can be used in numerous applications. Finding frequent patterns in huge databases is important because it reveals important information that cannot be found through simple data surfing. To find common patterns, a variety of methods are utilized, each of which performs differently. Apriori and FP Growth are the fundamental algorithms employed in frequent pattern mining. The functioning and experimental results of various algorithms are compared in this study, and their benefits and drawbacks are discussed.

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Joseph, Jismy, and Kesavaraj G. "Evaluation of Frequent Itemset Mining Algorithms-Apriori and FP Growth." International Journal of Engineering Technology and Management Sciences 4, no. 6 (September 28, 2020): 1–4. http://dx.doi.org/10.46647/ijetms.2020.v04i06.001.

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Nowadays the Frequentitemset mining (FIM) is an essential task for retrieving frequently occurring patterns, correlation, events or association in a transactional database. Understanding of such frequent patterns helps to take substantial decisions in decisive situations. Multiple algorithms are proposed for finding such patterns, however the time and space complexity of these algorithms rapidly increases with number of items in a dataset. So it is necessary to analyze the efficiency of these algorithms by using different datasets. The aim of this paper is to evaluate theperformance of frequent itemset mining algorithms, Apriori and Frequent Pattern (FP) growth by comparing their features. This study shows that the FP-growth algorithm is more efficient than the Apriori algorithm for generating rules and frequent pattern mining.

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Chang, Tsui-Ping. "A Sliding-Window Method to Discover Recent Frequent Query Patterns from XML Query Streams." International Journal of Software Engineering and Knowledge Engineering 24, no. 06 (August 2014): 955–80. http://dx.doi.org/10.1142/s021819401450034x.

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Providing efficient mining algorithm to discover recent frequent XML user query patterns is crucial, as many applications use XML to represent data in their disciplines over the Internet. These recent frequent XML user query patterns can be used to design an index mechanism or cached and thus enhance XML query performance. Several XML query pattern stream mining algorithms have been proposed to record user queries in the system and thus discover the recent frequent XML query patterns over a stream. By using these recent frequent XML query patterns, the query performance of XML data stream is improved. In this paper, user queries are modeled as a stream of XML queries and the recent frequent XML query patterns are thus mined over the stream. Data-stream mining differs from traditional data mining since its input of mining is data streams, while the latter focuses on mining static databases. To facilitate the one-pass mining process, novel schemes (i.e. XstreamCode and XstreamList) are devised in the mining algorithm (i.e. X2StreamMiner) in this paper. X2StreamMiner not only reduces the memory space, but also improves the mining performance. The simulation results also show that X2StreamMiner algorithm is both efficient and scalable. There are two major contributions in this paper. First, the novel schemes are proposed to encode and store the information of user queries in an XML query stream. Second, based on the two schemes, an efficient XML query stream mining algorithm, X2StreamMiner, is proposed to discover the recent frequent XML query patterns.

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Thiet, Pham Thi. "APPLYING THE ATTRIBUTED PREFIX TREE FOR MINING CLOSED SEQUENTIAL PATTERNS." Vietnam Journal of Science and Technology 54, no. 3A (March 20, 2018): 106. http://dx.doi.org/10.15625/2525-2518/54/3a/11964.

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Mining closed sequential patterns is one of important tasks in data mining. It is proposed to resolve difficult problems in mining sequential pattern such as mining long frequent sequences that contain a combinatorial number of frequent subsequences or using very low support thresholds to mine sequential patterns is usually both time- and memory-consuming. This paper applies the characteristics of closed sequential patterns and sequence extensions into the prefix tree structure to mine closed sequential patterns from the sequence database. The paper uses the parent–child relationship on prefix tree structure and each node on prefix tree is also added fields to determine whether that is a closed sequential pattern or not. Experimental results show that the number of sequential patterns is reduced significantly.

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Xie, Shiyong, and Long Zhao. "An Efficient Algorithm for Mining Stable Periodic High-Utility Sequential Patterns." Symmetry 14, no. 10 (September 28, 2022): 2032. http://dx.doi.org/10.3390/sym14102032.

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Periodic high-utility sequential pattern mining (PHUSPM) is used to extract periodically occurring high-utility sequential patterns (HUSPs) from a quantitative sequence database according to a user-specified minimum utility threshold (minutil). A sequential pattern’s periodicity is determined by measuring when the frequency of its periods (the time between two consecutive happenings of the sequential pattern) exceed a user-specified maximum periodicity threshold (maxPer). However, due to the strict judgment threshold, the traditional PHUSPM method has the problem that some useful sequential patterns are discarded and the periodic values of some sequential patterns fluctuate greatly (i.e., are unstable). In frequent itemset mining (FIM), some researchers put forward some strategies to solve these problems. Because of the symmetry of frequent itemset pattern (FIPs), these strategies cannot be directly applied to PHUSPM. In order to address these issues, this work proposes the stable periodic high-utility sequential pattern mining (SPHUSPM) algorithm. The contributions made by this paper are as follows. First, we introduce the concept of stability to overcome the abovementioned problems, mine sequential patterns with stable periodic behavior, and propose the concept of stable periodic high-utility sequential patterns (SPHUSPs) for the first time. Secondly, we design a new data structure named the PUL-list to record the periodic information of sequential patterns, thereby improving the mining efficiency. Thirdly, we propose the maximum lability pruning strategy in sequential pattern (MLPS), which can prune a large number of unstable sequential patterns in advance. To assess the algorithm’s effectiveness, we perform many experiments. It turns out that the algorithm can not only mine patterns that are ignored by traditional algorithms, but also ensure that the discovered patterns have stable periodic behavior. In addition, after using the MLPS pruning strategy, the algorithm can prune 46.5% of candidates in advance on average in six datasets. Pruning a large number of candidates in advance not only speeds up the mining process, but also greatly reduces memory usage.

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Madan Kumar, K. M. V., and B. Srinivasa Rao. "Mining Frequent Utility Sequential Patterns in Progressive Databases by U-Pisa." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1786–95. http://dx.doi.org/10.1166/jctn.2020.8442.

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Sequential pattern mining is one of the most important aspects of data mining world and has a significant role in many applications like market analysis, biomedical analysis, weather forecasting etc. In the category of mining sequential patterns the usage of progressive database as an input database is relatively new and has a wide impact in decision-making system. In progressive sequential pattern mining, we discover the frequent sequences progressively with the help of period of Interest. As the traditional approaches of frequency based framework are not much more informative for decision making, in recent effort utility framework has been incorporated instead of frequency. This addressed many typical business concerns such as profit value associated with each pattern. In this paper, we applied the concept of frequent utility over the progressive database and discovered the sequential pattern efficiently. To do so we proposed an algorithm called U-Pisa which works progressively with the help of a quantitative progressive database. We conducted sub-stantial experiments on the proposed algorithm and proved that this process performs well.

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Chang, Ye-In, Jun-Hong Shen, Chia-En Li, Zih-Siang Chen, and Ming-Hsuan Tu. "Mining image frequent patterns based on a frequent pattern list in image databases." Journal of Supercomputing 76, no. 4 (October 19, 2019): 2597–621. http://dx.doi.org/10.1007/s11227-019-03041-y.

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Zou, Qinghua, Wesley Chu, David Johnson, and Henry Chiu. "A Pattern Decomposition Algorithm for Data Mining of Frequent Patterns." Knowledge and Information Systems 4, no. 4 (September 27, 2002): 466–82. http://dx.doi.org/10.1007/s101150200016.

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Bashir, Shariq, and Daphne Teck Ching Lai. "Mining Approximate Frequent Itemsets Using Pattern Growth Approach." Information Technology and Control 50, no. 4 (December 16, 2021): 627–44. http://dx.doi.org/10.5755/j01.itc.50.4.29060.

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Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

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Mangesh Ghonge, Prof, and Miss Neha Rane. "Mining Rare Patterns by Using Automated Threshold Support." International Journal of Engineering & Technology 7, no. 3.8 (July 7, 2018): 77. http://dx.doi.org/10.14419/ijet.v7i3.8.15225.

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Essentially the most primary and crucial part of data mining is pattern mining. For acquiring important corre-lations among the information, method called itemset mining plays vital role Earlier, the notion of itemset mining was used to acquire the absolute most often occurring items in the itemset. In some situation, though having utility value less than threshold it is necessary to locate such items because they are of great use. Considering the thought of weight for each and every apparent items brings effectiveness for mining the pattern efficiently. Different mining algorithms are utilized to obtain the correlations among the information items based on frequency with the items in the dataset occurs. In frequent itemset, those things which occurs frequently whereas, in infrequent itemset the items that occur very rarely are obtained. Determining such form of data is tougher than to locate data which occurs frequently. Frequent Itemset Mining (FISM) locates large and frequent itemsets in huge data for example market baskets. Such data has two properties that are not addressed by FISM; Mixture property and projection property. Here the proposed system combines both mixture as well as projection property further providing automated support thresholds.

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Gour, Aastha. "A Review on Different Techniques for Mining Frequent Patterns in Unordered Trees." Mathematical Statistician and Engineering Applications 70, no. 2 (February 26, 2021): 1686–94. http://dx.doi.org/10.17762/msea.v70i2.2459.

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Numerous fields, such as bioinformatics, web mining, and social network analysis, now require frequent pattern mining in unordered trees. Finding repeating patterns and substructures inside a collection of unordered trees is the key to unlocking this technique's potential to provide important information about the underlying data. This paper gives a thorough overview of various methods for mining frequent patterns in unordered trees, highlighting their advantages, disadvantages, and practical uses.The review starts out by defining the basic terms and concepts related to frequent pattern mining in unordered trees. The discussion then moves on to a number of widely used algorithms in this setting, such as graph-based strategies, bottom-up tree traversal techniques, and depth-first search-based techniques. Each approach is thoroughly explained, including its underlying concepts, computational complexity, and applicability to different kinds of tree datasets.The paper also examines recent developments in the subject, including distributed frameworks and scalable parallel algorithms for mining common patterns in big unordered tree collections. In order to improve the mining process and the calibre of patterns found, it also looks at the incorporation of extra constraints and measurements like weighted support and tree edit distance.The paper also talks about recent developments in the subject, namely scalable parallel algorithms and distributed frameworks for finding common patterns in enormous unordered tree collections. In order to strengthen the mining process and raise the calibre of patterns found, it also looks at the incorporation of extra restrictions and metrics like tree edit distance and weighted support.

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Lin, Chun-Cheng, Wei-Ching Li, Ju-Chin Chen, Wen-Yu Chung, Sheng-Hao Chung, and Kawuu W. Lin. "A Distributed Algorithm for Fast Mining Frequent Patterns in Limited and Varying Network Bandwidth Environments." Applied Sciences 9, no. 9 (May 6, 2019): 1859. http://dx.doi.org/10.3390/app9091859.

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Data mining is a set of methods used to mine hidden information from data. It mainly includes frequent pattern mining, sequential pattern mining, classification, and clustering. Frequent pattern mining is used to discover the correlation among various sets of items within large databases. The rapid upward trend in data size slows the mining of frequent patterns. Numerous studies have attempted to develop algorithms that operate in distributed computing environments to accelerate the mining process. FLR-mining (Fast, Load balancing and Resource efficient mining algorithm) is one of the fastest methods of mining with efficient consideration of load balancing and resources. FLR-mining can automatically determine the appropriate number of computing nodes. However, FLR-mining and existing methods assume that the network bandwidth is constant. In practical distributed and many-task computing systems, this assumption fails because there are packet collisions caused by many mining tasks that run in a simultaneous manner. Therefore, a method that can consider the varying network bandwidth is necessary. In this study, we propose a method that can rapidly mine frequent patterns under the varying network bandwidth. The proposed method can also determine the appropriate number of computing nodes to efficiently utilize computing resources and achieve load balancing. Through empirical evaluation, the proposed method is shown to deliver excellent performance in terms of execution efficiency and load balancing.

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Ding, Jiaman, Yunpeng Li, Ling Li, and Lianyin Jia. "Prefix-Pruning-Based Distributed Frequent Trajectory Pattern Mining Algorithm." Mathematical Problems in Engineering 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/3838147.

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An important problem to be solved in smart city construction is how to improve the efficiency of mining frequent patterns that can be used for location prediction and location-based services of massive trajectory datasets. Owing to uncertain personal trajectory and non-explicit trajectory items, the existing sequence mining algorithms cannot be used directly. To solve this problem, this study proposes a distributed trajectory frequent pattern mining algorithm (SparkTraj) based on prefix pruning. First, a grouping and partitioning technique is used to abstract the original trajectory data and convert them into a common time series.Then, the generation of a redundant trajectory pattern is avoided by using the path adjacency pruning method. Second, to improve mining efficiency, SparkTraj is designed and implemented in Spark, which employs cluster memory computing. Finally, experiments on common datasets show that the proposed algorithm can effectively extract frequent trajectory patterns, and, in particular, deal with the massive amounts of trajectory data. Compared with common trajectory pattern mining algorithms, the SparkTraj algorithm not only improves the overall performance but also has good scalability.

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Alasow, Abdirahman, and Marek Perkowski. "Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining." Journal of Quantum Information Science 13, no. 01 (2023): 1–23. http://dx.doi.org/10.4236/jqis.2023.131001.

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Poonam Sengar, Poonam Sengar. "Discovering Frequent Patterns with New Mining Procedure." IOSR Journal of Computer Engineering 10, no. 5 (2013): 32–37. http://dx.doi.org/10.9790/0661-1053237.

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RONG, Wen-liang. "Mining frequent closed patterns over data stream." Journal of Computer Applications 28, no. 6 (August 20, 2008): 1467–70. http://dx.doi.org/10.3724/sp.j.1087.2008.01467.

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ZOU, Zhao-Nian, Jian-Zhong LI, Hong GAO, and Shuo ZHANG. "Mining Frequent Subgraph Patterns from Uncertain Graphs." Journal of Software 20, no. 11 (November 30, 2009): 2965–76. http://dx.doi.org/10.3724/sp.j.1001.2009.03473.

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Pan, Jeng-Shyang, Jerry Chun-Wei Lin, Lu Yang, Philippe Fournier-Viger, and Tzung-Pei Hong. "Efficiently mining of skyline frequent-utility patterns." Intelligent Data Analysis 21, no. 6 (November 15, 2017): 1407–23. http://dx.doi.org/10.3233/ida-163180.

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Silvestri, Claudio, and Salvatore Orlando. "Approximate mining of frequent patterns on streams." Intelligent Data Analysis 11, no. 1 (March 15, 2007): 49–73. http://dx.doi.org/10.3233/ida-2007-11104.

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Wang, Wei. "Mining Frequent Patterns Based on Graph Theory." Journal of Computer Research and Development 42, no. 2 (2005): 230. http://dx.doi.org/10.1360/crad20050208.

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Laur, Pierre-Alain, Richard Nock, Jean-Emile Symphor, and Pascal Poncelet. "Mining evolving data streams for frequent patterns." Pattern Recognition 40, no. 2 (February 2007): 492–503. http://dx.doi.org/10.1016/j.patcog.2006.03.006.

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Amphawan, Komate, and Philippe Lenca. "Mining top-k frequent-regular closed patterns." Expert Systems with Applications 42, no. 21 (November 2015): 7882–94. http://dx.doi.org/10.1016/j.eswa.2015.06.021.

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Journal articles on the topic 'Mining Frequent Patterns'

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