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...

Transmission network expansion planning (TNEP) is an important component of power system planning. Itdetermines the characteristics and performance of the future electric power network and influences the powersystem operation directly. Different methods have been proposed for the solution of the static transmissionnetwork expansion planning (STNEP) problem till now. But in all of them, STNEP pr...

There are number of existing algorithms proposed that mines frequent patterns from certain or precise data. But know a day’s demand of uncertain data mining is increased. There are many situations in which data are uncertain. For frequent pattern mining from uncertain data mainly two approaches are proposed that are level-wise approach and pattern-growth approach. Level-wise approach use the ge...

Exploring frequent itemset from huge transactional database has been the most time consuming process of association rule mining.Up-to-date, various algorithms have been popularized in the area of frequent itemset generation. The FP-growth algorithms are the most familiar algorithms. FP-growth algorithm adopts tree structure for storing information producing in longer runtime. FP-growth algorith...