TY  - JOUR
TI  - Implementation of Improved K-Mean Algorithm for Intrusion Detection System to Improve the Detection Rate
AU  - Susheel Kumar Tiwari
AU  -  Dr. Manish Shrivastava
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2018/02/28
PY  - 2018
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT1831141
VL  - 3
IS  - 1
SP  - 649	
EP  - 654
AB  - In Data mining there are lots of methods are used to detect the outlier by making the clusters of data and then detect the outlier from them. In general Clustering method plays a very important role in data mining. Clustering means grouping the similar data objects together based on the characteristic they possess. An improved K-means clustering algorithm is put forward on basis of the split-merge method for the purpose of remedying defects both in determination of value in K and in selection of initial cluster centre of traditional K-means clustering. At first , the concept of independence degree of date was incorporated into the experimental date subset construction theory , using independence degree to evaluate the importance of nature. Next ,the database is merged into several classes in respect of density of date points ,the combination of the minimum spanning tree algorithm and traditional K-means clustering algorithm is conducive to the achievement of splitting .Eventually ,the KDD Cup99 database is applied to conduct simulation experiment on the application of the improved algorithm in intrusion detection .The results indicate that the improved algorithm prevails over traditional K-means algorithm in detection rate and false alarm rate