Mining Maximal Frequent Itemsets 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, A.P, India.
Manuscript received on 12 April 2019 | Revised Manuscript received on 19 May 2019 | Manuscript published on 30 May 2019 | PP: 2116-2122 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1901058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Today, most of the data mining applications exhibiting high data flow rate, and expecting algorithms to match the flow rate with redundant less knowledge. Data streaming applications consider every incoming transaction as a new tuple, irrespective of whether it is old tuple that gets revised or not. This kind of revision in data streaming application gives new and hidden knowledge, also brings new challenges and issues to the tasks. One of the issue is, interested/frequent itemsets may turn to infrequent or infrequent itemsets may turned into frequent, and other one is redundancy in output. In this paper, we address solution to the redundancy in output by finding maximal itemsets from tuple revision data streams. We propose SlideTree data structure to maintain stream data, and Lattice-Tree to maintain maximal itemset information. We propose an Update algorithm that combines effective data structures that derives all the Maximal itemsets over the tuple evolving data streams.
Keywords: Data Streams, Slide Tree, Tuple-Evolving Data Streams.
Scope of the Article: Data Mining