Mining Closed Item sets from Tuple-Evolving Data Streams
Bhargavi Peddireddy1, Ch. Anuradha2, P.S.R. Chandra Murthy3

1Bhargavi Peddireddy, Department of Computer Science and Engineering, Anucet, Acharya Nagarjuna University, Guntur, A.P, India.
2Ch. Anuradha, Assistant Professor, Department of Computer Science and Engineering, V.R. Siddhartha Engineering College, Vijayawada, A.P, India.
3P.S.R.Chandra Murthy, Department of Computer Science and Engineering, Anucet, Acharya Nagarjuna University, Guntur, AP, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 103-105 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9107088619/2019©BEIESP | DOI: 10.35940/ijeat.F9107.088619
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Abstract: Frequent Itemset Mining is playing major role in extracting useful knowledge from data streams that are exhibiting high data flow. Studies in data streams shows that every incoming data is considered as new tuple which is considered as revised tuple in some applications called as tuple evolving data streams. Extracting redundant less knowledge from such kind of application helps in better decision making with new challenges. One of the issue is, due to incoming revised tuple, some of the frequent items ets may turn to infrequent or previously ignore item sets may become frequent. Other issue is result of FIM may be huge and redundant results. In this paper, we address solution to the problem by finding closed item sets from tuple revision data streams. We propose an efficient approach MCST that uses compressed Slide Tree data structure to maintain stream data, propose HIS hash table to maintain item sets, and CIS tables to maintain closed id sets to improve search performance of HIS.
Keywords: CT lung Coronel, Wavelet, Encoding, features, GLCM and Classification.