Distributed Mining of High Utility Time Interval Sequential Patterns with Multiple Minimum Utility Thresholds

Saleti, Sumalatha; Tangirala, Jaya Lakshmi; Ragunathan, T.

doi:10.1007/978-3-030-79457-6_8

Cited by 2 publications

(1 citation statement)

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“…In light of this, DHUTISP [20] has been introduced, it is a distributed MapReduce algorithm. Recently, a MapReduce algorithm, namely, DHUTISP-MMU [21] has been proposed to handle the issue of multiple utility of items. But, it considers the predefined time interval set before generating the patterns.…”

Section: Introductionmentioning

confidence: 99%

Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

2022

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Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of efficiency, memory utilization, and scalability. INDEX TERMSData mining, MapReduce framework, Multiple utility thresholds, Sequential pattern mining, Time interval patterns.

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Section: Introductionmentioning

confidence: 99%