Intrusion detection in wireless networks has become a vital part in wireless network security systems with wide spread use of Wireless Local Area Networks (WLAN). Currently, almost all devices are Wi-Fi (Wireless Fidelity) capable and can access WLAN. This paper proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to w...

Classification is the supervised learning algorithm that maps the data into predefined groups & classes. Clustering is unsupervised algorithm which finds groups of objects such that the objects in one group will be similar to one another and different from the objects in another group while Association Rule algorithms are used to show the relationship between the data items. In this paper we pr...

The Apriori algorithm that mines frequent itemsets is one of the most popular and widely used data mining algorithms. Now days many algorithms have been proposed on parallel and distributed platforms to enhance the performance of Apriori algorithm. They differ from each other on the basis of load balancing technique, memory system, data decomposition technique and data layout used to implement ...

-With the recent explosive growth of the amount of data content and information on the Internet, it has become increasingly difficult for users to find and maximize the utilization of the information found in the internet. Traditional web search engines often return hundreds or thousands of results for a particular search, which is time consuming .In order to overcome these problems, we have de...

In data mining frequent item-sets mining is one of the important tasks. Apriori is used to mine the frequent item-sets but, Apriori also has some problem as in Apriori finding of support count is very time consuming procedure. To overcome this problem of Apriori, BitApriori algorithm is proposed for mining frequent item-sets, but the BitApriori also suffer the problem of memory scarcity when th...

We investigate large scale probabilistic association mining on modest hardware infrastructure. We first propose a probabilistic columnar infrastructure for storing the transaction database. Using Bloom filters and reservoir sampling techniques, the storage is e cient and probabilistic. Then we propose an accurate probabilistic algorithm for mining frequent item-sets. Our algorithm relies on the...

ISSN 2277 5048 | © 2014 Bonfring Abstract--Computer Systems are exposed to an increasing number of different types of security threats due to the expanding of internet in recent years. How to detect network intrusions effectively becomes an important security technique. Many intrusions aren’t composed by single events, but by a series of attack steps taken in chronological order. Analyzing the ...

In this paper we investigate the relationship between closed itemset mining, the complete pruning technique and item ordering in the Apriori algorithm. We claim, that when proper item order is used, complete pruning does not necessarily speed up Apriori, and in databases with certain characteristics, pruning increases run time significantly. We also show that if complete pruning is applied, the...

A data cutting and sorting method (DCSM) is proposed to optimize the performance of data mining. DCSM reduces the calculation time by getting rid of redundant data during the data mining process. In addition, DCSM minimizes the computational units by splitting the database and by sorting data with support counts. In the process of searching for the relationship between metabolic syndrome and li...

Association rule mining is the most popular technique in the area of data mining. The main task of this technique is to find the frequent patterns by using minimum support thresholds decided by the user. The Apriori algorithm is a classical algorithm among association rule mining techniques. This algorithm is inefficient because it scans the database many times. Second, if the database is large...