Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data Stream

Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2 fuzzy set theory over data stream (WSWFFP-T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type-2 fuzzy set theory (WFFPT2-tree) and fuzzy-list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FFPs) over the medical data stream. The experiments show that the proposed WSWFFP-T2 algorithm is optimal for mining the quantitative data stream and not limited to the fragile databases; the performance is reliable and stable under the condition of the weighted sliding window. Moreover, the proposed algorithm has high performance in mining the FFPs compared with the existing algorithms under the condition of recall and precision rates.

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An Efficient Approach for Frequent Pattern Mining Method Using Fuzzy Set Theory

Proceedings of the International Congress on Information and Communication Technology - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-10-0755-2_17 ◽

2016 ◽

pp. 151-160

Author(s):

Manmay Badheka ◽

Sagar Gajera

Keyword(s):

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Mining Method ◽

Efficient Approach

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Mining Closed Weighed Frequent Patterns from a Sliding Window over Data Stream

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.2606 ◽

2013 ◽

Vol 756-759 ◽

pp. 2606-2609

Author(s):

Cui Cui Ge ◽

Xiu Fen Fu

Keyword(s):

Data Stream ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Itemset ◽

Experimental Results ◽

Frequent Pattern ◽

Frequent Patterns ◽

Itemset Mining ◽

Basic Window

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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Framework for High Utility Pattern Mining using Dynamically Generated Minimum Support ThresholdFramework for High Utility Pattern Mining using Dynamically Generated Minimum Support Threshold

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.19.28276 ◽

2018 ◽

Vol 7 (4.19) ◽

pp. 1007

Author(s):

Shankar B. Naik ◽

Jyoti D. Pawar

Keyword(s):

Data Stream ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Pattern ◽

Minimum Support ◽

Itemset Mining ◽

Support Threshold ◽

High Utility

In this paper we have proposed a framework which uses high utility itemset mining to store data stream elements in a compressed form and then detect events from the sliding window. This approach promises to reduce the memory requirements when applied to frequent pattern mining in data streams.In addition to this, a method to dynamically define the value of minimum support threshold based on data in the data stream is presented.

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Sliding window based weighted maximal frequent pattern mining over data streams

Expert Systems with Applications ◽

10.1016/j.eswa.2013.07.094 ◽

2014 ◽

Vol 41 (2) ◽

pp. 694-708 ◽

Cited By ~ 64

Author(s):

Gangin Lee ◽

Unil Yun ◽

Keun Ho Ryu

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Pattern ◽

Maximal Frequent Pattern

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EclatDS: An efficient sliding window based frequent pattern mining method for data streams

Intelligent Data Analysis ◽

10.3233/ida-2011-0483 ◽

2011 ◽

Vol 15 (4) ◽

pp. 571-587 ◽

Cited By ~ 11

Author(s):

Mahmood Deypir ◽

Mohammad Hadi Sadreddini

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Pattern ◽

Mining Method

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An efficient Algorithm for Frequent Pattern Mining over Uncertain Data Stream

2019 12th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid.2019.00026 ◽

2019 ◽

Author(s):

Mingye Xie ◽

Long Tan

Keyword(s):

Efficient Algorithm ◽

Data Stream ◽

Pattern Mining ◽

Uncertain Data ◽

Frequent Pattern Mining ◽

Frequent Pattern

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Sliding window-based frequent pattern mining over data streams

Information Sciences ◽

10.1016/j.ins.2009.07.012 ◽

2009 ◽

Vol 179 (22) ◽

pp. 3843-3865 ◽

Cited By ~ 85

Author(s):

Syed Khairuzzaman Tanbeer ◽

Chowdhury Farhan Ahmed ◽

Byeong-Soo Jeong ◽

Young-Koo Lee

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Pattern

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An Efficient Frequent Patterns Mining Algorithm over Data Streams Based on FPD-Graph

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.4457 ◽

2012 ◽

Vol 433-440 ◽

pp. 4457-4462 ◽

Cited By ~ 1

Author(s):

Jun Shan Tan ◽

Zhu Fang Kuang ◽

Guo Gui Yang

Keyword(s):

Data Streams ◽

Data Stream ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Frequent Patterns ◽

Data Generation ◽

Experiment Data ◽

Mining Algorithm ◽

Head Node

The design of synopses structure is an important issue of frequent patterns mining over data stream. A data stream synopses structure FPD-Graph which is based on directed graph is proposed in this paper. The FPD-Graph contains list head node FPDG-Head and list node FPDG-Node. The operations of FPD-Graph consist of insert operation and deletion operation. A frequent pattern mining algorithm DGFPM based on sliding window over data stream is proposed in this paper. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The DGFPM algorithm not only has high precision for mining frequent patterns, but also has low processing time.

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