A Self Developing Element Clustering Approach for Oddity Discovery

To detect attacks from IOT[1] and big data application using data mining techniques. Now-a-days internet can be access from anywhere using small devices such as smart phones, sensors [4] and other wearable devices etc. Always these devices will sense data such as human body[7] temperature or environment temperature or traffic data at road side etc and send this sense data to centralized server for aggregation (storage). Later this data will be used for analysis purpose such as to detect patient condition from sense patient data or to identify traffic[6] congested area. Humans will be benefitted by using above sensors and internet technologies but these will aggregate lots of data and will be called as big data and normal technique will not process such huge data and other problem is some malicious users will corrupt sensor data by attacking network or injecting extra data inside sensor sense data packet. To overcome from this problem[8], within document we are introduced a technique called CLAPP. In this technique big data attributes will be reduce by applying Dimensionality Reduction Technique. This technique will take entire data and check each column (attribute) similarity with other column and generate cluster based on similarity. If two column values are similar and related to given class then it will clustered and if not similar then that attribute will be remove out to reduce big dataset size.