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Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size

by V.sidda Reddy, T.v. Rao, A.govardhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 2
Year of Publication: 2014
Authors: V.sidda Reddy, T.v. Rao, A.govardhan
10.5120/17662-8479

V.sidda Reddy, T.v. Rao, A.govardhan . Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size. International Journal of Computer Applications. 101, 2 ( September 2014), 34-40. DOI=10.5120/17662-8479

@article{ 10.5120/17662-8479,
author = { V.sidda Reddy, T.v. Rao, A.govardhan },
title = { Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 2 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number2/17662-8479/ },
doi = { 10.5120/17662-8479 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:40.508667+05:30
%A V.sidda Reddy
%A T.v. Rao
%A A.govardhan
%T Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 2
%P 34-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Continuous prediction of closed frequent itemsets from high speed distributed data streams is an active research work, which is because of the conflict to the process time taken to perform mining consistent itemsets from current records and high alacrity transmission time in data streams. By the motivation gained from our earlier proposed models, here we devised a novel closed frequent itemset mining model for high speed distributed data streams. The said model is referred as Parallel Closed Frequent Itemsets Mining (PCFIM) over High Speed Distributed Data streams by Manifold Varying Size Windows (MVSW). The results obtained from experiments are significant to prove that the proposed PCFIM is scalable and robust on high speed data streams and miles ahead over existing bench mark models.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Streams Distributed Data Stream Closed Frequent Itemsets Mining Sliding Window Varying Window.

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