The Analysis, Based On Data Mining Algorithms For Frequent Pattern Tree

Data Mining is an information processing technology, which is developing very fast in recent years. Using data mining, people can abstract information and knowledge from a great deal of data which is incomplete, noisy, dark and random. The information and knowledge we got was ignored and had not been known before but potentially useful. Data Mining is a technique that discovers the interesting, hidden, and unknown knowledge from large data. It has emerged as one of the most promising areas for database research over the past decade. Data mining involves an integration of techniques from database, artificial intelligence,machine learning, pattern recognition, knowledge engineering, object-orientedmethod, information retrieval, high performance computing and visualization.Association rule mining is an important sub-branch of the Data Mining. Its role finds out strong rules if they satisfy both a minimum support threshold and aminimum confidence threshold. In the background of data mining and association rule mining, the thesis conducts research and application on the method of association rule mining. Association rule mining has become a hot research topic in recent years, and it has been used widely in selective marketing, decision analysis and business management. Association rule mining algorithms are the core contents in the area. So far, there are several famous typical algorithms.This article introduced the definition and the main technologies of Data Mining at first, then described the association rules mining theory and the algorithm in detail, including classical Apriori algorithm and FP-Growth algorithm, and these algorithm's instances provided to analyze two algorithm.Finally, the optimized algorithm given on two aspects of optimizing the data structure and dividing the large datasets into many subsets, and then, carry out frequent itemsets mining for each subset and optimize the FP-Growth algorithm. Experiments have been conducted to compare the optimizedalgorithms more effective than FP-Growth when they are mining large datasets.