FREQUENT PATTERN MINING OVER DATA STREAMS WITHIN TIME SENSITIVE SLIDING WINDOW MODEL BASED PREFIX-TREE AND PROBABILISTIC ESTIMATION

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DEYPIR MAHMOOD; DALILI OSKOEI HAMIDREZA

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Journal: ELECTRONIC INDUSTRIES Year:2017 | Volume:7 | Issue:4 Page(s): 71-84

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Title

FREQUENT PATTERN MINING OVER DATA STREAMS WITHIN TIME SENSITIVE SLIDING WINDOW MODEL BASED PREFIX-TREE AND PROBABILISTIC ESTIMATION

Author(s)

DEYPIR MAHMOOD | DALILI OSKOEI HAMIDREZA | Issue Writer Certificate

Keywords

DATA STREAMQ2

TIME SENSITIVE SLIDING WINDOWQ2

FREQUENT ITEMSET MININGQ2

PROBABILISTIC ESTIMATIONQ2

Abstract

Mining frequent patterns over DATA STREAMs is a challenging problem due to speed of input streams in real applications, processing and storage limitations. There are various models for mining frequent patterns over DATA STREAMs. TIME SENSITIVE SLIDING WINDOW model is preferable due to modeling both concept change and varying speed of input data. Adding and removing transactions to/from sliding window leads to change in the set of frequent patterns. Approach to compute or approximate the frequency of new item sets has a direct effect on the efficiency of the mining algorithm. In this study, for first time, a PROBABILISTIC ESTIMATION is used to approximate the support values for new frequent item sets. Based on this approximation, a new algorithm is proposed which can mine the set of frequent pattern within a TIME SENSITIVE SLIDING WINDOW. This algorithm benefits from a novel prefix tree based data structure to store the set of frequent patterns of the active window. Experimental evaluations performed on real life and synthetically generated datasets show the superiority of the proposed algorithm with respect to previously proposed approaches in terms of memory usage and runtime.

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APA: Copy

DEYPIR, MAHMOOD, & DALILI OSKOEI, HAMIDREZA. (2017). FREQUENT PATTERN MINING OVER DATA STREAMS WITHIN TIME SENSITIVE SLIDING WINDOW MODEL BASED PREFIX-TREE AND PROBABILISTIC ESTIMATION. ELECTRONIC INDUSTRIES, 7(4), 71-84. SID. https://sid.ir/paper/229541/en

Vancouver: Copy

DEYPIR MAHMOOD, DALILI OSKOEI HAMIDREZA. FREQUENT PATTERN MINING OVER DATA STREAMS WITHIN TIME SENSITIVE SLIDING WINDOW MODEL BASED PREFIX-TREE AND PROBABILISTIC ESTIMATION. ELECTRONIC INDUSTRIES[Internet]. 2017;7(4):71-84. Available from: https://sid.ir/paper/229541/en

IEEE: Copy

MAHMOOD DEYPIR, and HAMIDREZA DALILI OSKOEI, “FREQUENT PATTERN MINING OVER DATA STREAMS WITHIN TIME SENSITIVE SLIDING WINDOW MODEL BASED PREFIX-TREE AND PROBABILISTIC ESTIMATION,” ELECTRONIC INDUSTRIES, vol. 7, no. 4, pp. 71–84, 2017, [Online]. Available: https://sid.ir/paper/229541/en

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