Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm the most that works on for finding frequent itemsets. Originally, Map-Reduce algorithm-based itemsets Hadoop were resolved. For handling big data, comes into picture but implementation of does not reach expectations distributed because its high I/O results tr...

In the current paper, we use an intelligent method for improved Apriori algorithm in order to extract frequent itemsets. PAA (proposed algorithm) is twofold. First, it not necessary take only one data item at each step. fact, all possible combinations of items could be generated Secondly, can scan some transactions instead scanning obtain itemset. For performance evaluation, conducted three exp...

Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environment. ASDD is based on features of the Apriori algorithm for mining association rules in large databases of sales transactions [1] and the MSDD algorithm for discovering stochastic dependencies in multiple streams of d...

Discovering the relationship between behavior and personality of learner in the web-based learning environment is a key to guide learners in the learning process. This paper proposes a new concept called personality mining to find the “deep” personality through the observed data about the behavior. First, a learner model which includes personality model and behavior model is proposed. Second, w...

In this paper a new mining algorithm is defined based on frequent item set. Apriori Algorithm scans the database every time when it finds the frequent item set so it is very time consuming and at each step it generates candidate item set. So for large databases it takes lots of space to store candidate item set .In undirected item set graph, it is improvement on apriori but it takes time and sp...

Mining association rules from databases is a time-consuming process. Finding the large item set fast is the crucial step in the association rule algorithm. In this paper we present a fast association rule algorithm (Bit-AssoRule) based on granular computing. Our Bit-AssocRule doesn’t follow the generation-and-test strategy of Apriori algorithm and adopts the divide-and-conquer strategy, thus av...