Distributed mining of high utility time interval sequential patterns using mapreduce approach

Saleti, Sumalatha; Subramanyam, R. B. V.

doi:10.1016/j.eswa.2019.112967

Cited by 28 publications

(7 citation statements)

References 41 publications

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“…Hybrid algorithms which finds patterns satisying more than one constraints result in generation of more useful knowledge [21]. Various hybrid algorithms have been proposed in the past such as UTMining [19], UTMining_A [43], TU-UFM [44], HUIPM [45], FDHUP [16,17], UIPrefixSpan [46] which consider more than one parameter during mining. The algorithm algorithm proposed in this paper is also a hybrid algorithm satisfying both support and utility as constraints in mining.…”

Section: B Utility Miningmentioning

confidence: 99%

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

Ritika

Gupta²

2022

IEEE Access

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The field of data mining is progressing rapidly presenting researchers with many opportunities for research. Sequential pattern mining is a popular and long established technique in data mining which extracts data in the form of sequential patterns satisfying a threshold value such as utility, support, profit or a combination of these. The application of fuzzy theory in sequential pattern mining has also been favored leading to more natural liinguistic representation. Researchers have proposed various hybrid algorithms with fuzzification of any one parameter such as time or quantity. This paper proposes a hybrid fuzzy algorithm for mining of frequent and high utility sequential patterns with fuzzification of both purchase time and purchase quantity parameters, thereby giving more useful fuzzy sequential patterns. Experimental results also prove that the proposed algorithm is better than the existing algorithms. INDEX TERMS Fuzzy theory, high utility mining, maximum measure, sequential pattern mining, timeintervals VOLUME 4, 2016

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Section: B Utility Miningmentioning

confidence: 99%

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

Ritika

Gupta²

2022

IEEE Access

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“…As the rapid growth for the research of HUSPM, it is necessary to develop an efficient model to discover the set of HUSPs from a large-scale efficiency. Sumalatha and Subramanyam [39] then presented a distributed high utility time interval sequential pattern mining (DHUTISP) algorithm based on the MapReduce framework. Two upper-bound models are then designed to reduce the computational cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model

Chun-Wei¹,

DjenouriYoucef²,

SrivastavaGautam³

et al. 2021

ACM Trans. Knowl. Discov. Data

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High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory of a single machine. In this article, we first develop a parallel and distributed three-stage MapReduce model for mining high-utility sequential patterns based on large-scale databases. Two properties are then developed to hold the correctness and completeness of the discovered patterns in the developed framework. In addition, two data structures called sidset and utility-linked list are utilized in the developed framework to accelerate the computation for mining the required patterns. From the results, we can observe that the designed model has good performance in large-scale datasets in terms of runtime, memory, efficiency of the number of distributed nodes, and scalability compared to the serial HUSP-Span approach.

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“…Kuang et al [67] proposed the parallel implementation of FP-Growth algorithm in Hadoop by removing the data redundancy between the different data partitions, which allows to handle the transactions in a single pass. Sumalatha et al [68] introduces the concept of distributed temporal high utility sequential patterns, and propose an intelligent strategy by creating a time interval utility data structure for evaluating the candidate patterns. The authors also defined two utility upper bounds, remaining utility, and co-occurrence utility to prune the search space.…”

Section: Related Workmentioning

confidence: 99%

A general-purpose distributed pattern mining system

et al. 2020

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This paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existing pattern mining problems by reducing the enumeration search space. Thus, it derives the relevant patterns by studying the different correlation among the transactions. It first decomposes the set of transactions into several clusters of different sizes, and then explores heterogeneous architectures, including MapReduce, single CPU, and multi CPU, based on the densities of each subset of transactions. To evaluate the DT-DPM framework, extensive experiments were carried out by solving five pattern mining problems (FIM: Frequent Itemset Mining, WIM: Weighted Itemset Mining, UIM: Uncertain Itemset Mining, HUIM: High Utility Itemset Mining, and SPM: Sequential Pattern Mining). Experimental results reveal that by using DT-DPM, the scalability of the pattern mining algorithms was improved on large databases. Results also reveal that DT-DPM outperforms the baseline parallel pattern mining algorithms on big databases.

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Distributed mining of high utility time interval sequential patterns using mapreduce approach

Cited by 28 publications

References 41 publications

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model

A general-purpose distributed pattern mining system

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