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Global High Dimension Outlier Algorithm for Efficient Clustering and Outlier Detection
| 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
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
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