A hybrid k-DCI and Apriori algorithm for mining frequent itemsets

Data mining involves analysis, extraction, refining and representation of required data from large databases. kDCI (k Direct Count and Intersect) algorithm is one of the best scalable algorithms for identifying frequent items in huge repository of data. This algorithm uses a special kind of compressed data structure which helps in mining the datasets easily. Apriori algorithm, a realization of frequent pattern matching, is universally adopted for reliable mining. It is based on parameters namely support and confidence. kDCI algorithm is hybridized with Apriori algorithm for better performance than their individual contribution. The result proves scalability and mining speed effectively.