Review of Frequent Itemsets Mining in High Dimensional Dataset

Nowadays, there are abundant of big data collection and to understand its patterns would need a thorough analysis. Analyzing big data would depend highly on the purpose and the tasks involved would be various. One of the significant tasks is frequent itemsets mining and the strategy has been evolved in many ways in order to improve the efficiency and effectiveness of the mining process. In this paper, we briefly reviewed mining frequent itemsets algorithms from year 1998 until year 2013 that focus on maximal and closed frequent itemsets. We discussed these algorithms based on three main areas namely: the searching strategy, space reduction method, and data representation. These three main areas are concluded as the optimization strategy and are designed to improve the efficiency and scalability using a different approach in different areas to adapt to numerous growth of the dataset. This work is beneficial for researchers in designing and enhancing the algorithm based on their own purposes.