In this paper, we propose three algorithms LCMfreq, LCM, and LCMmax for mining all frequent sets, frequent closed item sets, and maximal frequent sets, respectively, from transaction databases. The main theoretical contribution is that we construct treeshaped transversal routes composed of only frequent closed item sets, which is induced by a parent-child relationship defined on frequent closed...

Data mining techniques have been widely used in various applications. Binary search tree based frequent items is an effective method for automatically recognize the most frequent items, least frequent items and average frequent items. This paper presents a new approach in order to find out frequent items. The word frequent item refers to how many times the item appeared in the given input. This...

Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent item sets, and then forming conditional implication rules among them. Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step i...

Frequent pattern mining is the process of mining data in a set of items or some patterns from a large database. The resulted frequent set data supports the minimum support threshold. A frequent pattern is a pattern that occurs frequently in a dataset. Association rule mining is defined as to find out association rules that satisfy the predefined minimum support and confidence from a given data ...