An Approach for Mining Frequent 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 (A.P), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 2039-2045 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6286058719/19©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, data streaming applications consider every incoming transaction as a new tuple.Most of the applications allows the tuples revision inside the streams over the time. This kind of revision in data streaming application gives new and hidden knowledge, also brings new challenges to the tasks. One of the issue is, frequent itemsets may become to infrequent and viceversa . To address this issue,We design efficient data structures to maintain stream data, information and candidate information of evolving tuples. We propose an algorithm that combines effective data structures that derives frequent itemsets over the tuple evolving data streams.
Keyword: Data Streams, SlideTree, HashTable, Tuple-Evolving Data Streams..
Scope of the Article: Data Mining.