A NEW ENERGY EFFICIENT VERTICAL HANDOVER ALGORITHM IN HETEROGENEOUS NETWORKS

Abstract

The mining of successive examples is a central part in numerous information mining undertakings. A lot of examination on this issue has prompted a wide arrangement of efficient and scalable algorithms for mining frequent patterns. On the other hand, discharging these examples is posturing worries on the protection of the clients’ participating in the data. In this proposition, we examine the mining of successive examples in a protection saving setting. We propose an approach for differential private frequent itemset mining which is based on LCM algorithm; we refer it as P-LCM algorithm. P-LCM is extended version on PFP growth algorithm which basically has two phases such as preprocessing and mining phase. The preprocessing phase needs to be performed only once and smart splitting method is used in this phase for improving utility as well as privacy trade off. Second phase limits the information loss caused by splitting as well as reduces the amount of noise added during mining process. In addition we propose three algorithms LCMfreq for mining all frequent sets instead of PFP-growth. LCM finds all frequent item sets in polynomial time per item set, and at the same time doesn’t store prior obtained closed item sets in memory. The computational experiments on real world and synthetic databases suggest on comparing their performance to the previous algorithms, that LCM algorithms are faster on large real dataset especially in case of high degree of privacy, high utility and high time efficiency.