HUFTI-SPM: high-utility and frequent time-interval sequential pattern mining from transactional databases

Ritika,; Gupta, Sunil Kumar

doi:10.1007/s41060-021-00297-7

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Liu presented a two phase approach for mining of itemsets with high utility [11,12]. To enhance the usefulness of patterns, various algorithms FDHUP [16,17], HUPSPM [18],UTMing [19], HUFTI-SPM [20] involving hybridization of constraints have been proposed. Algorithms involving hybridization of constraints mine best knowledge by generating smaller pattern set in lesser execution time [21].…”

Section: Introductionmentioning

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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