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Global High Dimension Outlier Algorithm for Efficient Clustering and Outlier Detection

by Nidhi Nigam, Tripti Saxena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 18
Year of Publication: 2015
Authors: Nidhi Nigam, Tripti Saxena
10.5120/ijca2015905363

Nidhi Nigam, Tripti Saxena . Global High Dimension Outlier Algorithm for Efficient Clustering and Outlier Detection. International Journal of Computer Applications. 131, 18 ( December 2015), 1-4. DOI=10.5120/ijca2015905363

@article{ 10.5120/ijca2015905363,
author = { Nidhi Nigam, Tripti Saxena },
title = { Global High Dimension Outlier Algorithm for Efficient Clustering and Outlier Detection },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 18 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number18/23546-2015905363/ },
doi = { 10.5120/ijca2015905363 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:27:41.038709+05:30
%A Nidhi Nigam
%A Tripti Saxena
%T Global High Dimension Outlier Algorithm for Efficient Clustering and Outlier Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 18
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this digital era most of the knowledge kinded on the market in digital form. For several years, individuals have command the hypothesis that exploitation phrases for square measure presentation of document and topic ought to perform higher than terms. During this paper we have a tendency to square measure examine and investigate this reality with considering many states of art data processing strategies that offers satisfactory results to boost the effectiveness of the pattern. Here we have a tendency to implementing pattern detection methodology to resolve downside of term-based strategies and improved result that useful in info retrieval systems. Our proposal is additionally evaluated for many well distinguish domain, providing all told cases, reliable taxonomies considering preciseness and recall in conjunction with F-measure. For the experiment, we'll use massive dataset and therefore the results ought to show that we have a tendency to improve the discovering pattern as compared to previous text mining strategies. The results of the experiment setup ought to show that the keyword-based strategies not offer higher performance than pattern-based methodology. The results additionally indicate that removal of vacuous patterns not solely reduces the price of computation however additionally improves the effectiveness of the system

References
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Index Terms

Computer Science
Information Sciences

Keywords

KDD DBSCAN Noisy data Distributed solving set Lazy distributed solving set.

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