New Algorithm For Mining Frequent Patterns Using Graph And Clique Algorithms

Frequent patterns are patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a dataset. In this paper we present a new algorithm to discover large itemset pattern. In this approach, the condensed data is used and is obtained by transforming into a clique problem. Firstly, the input dataset is transformed into a graph-based structure and then we find cliques as candidate patterns. In this approach the number of candidate patterns is less than other algorithms, so this new algorithm is fast and accurate and because of using graph and it is easy and simple to update graph so this algorithm is more flexible. The computational results show large itemset patterns with good scalability properties.

Keywords:

frequent pattern , data mining , clique