期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2015
卷号：6
期号：1
页码：52-56
语种：English
出版社：Ayushmaan Technologies
摘要：Feature selection involves identifying a subset of the most representative features.Feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view.Based on these criteria, a fast clustering-based feature selection algorithm (FAST) is proposed.The Feature Subset Selection generally works in two steps:Features are divided into clusters by using graphtheoretic clustering methods. The most representative feature that is strongly related to target class is selected.To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method.There are several algorithms applied to find the efficiency and effectiveness. Here we consider the efficiency as the time taken to retrieve the data’s from the database and effectiveness is from the most datasets (or) subsets which are relevant to the users search. By using FAST algorithm we can retrieve the data’s without the irrelevant features. Here the irrelevant features are carried out by means of various levels of the query input and the output the relevant information can be carried out in case of the subset selection and clustering methods.
