Artificial Immune Algorithm And Its Applications In Data Mining

The arrival of the information age will undoubtedly have a profound influence on our lives. It brings extensive quantity of data in which many important information and knowledge hide. How to get deep rules from data is the urgent problem which needs to be worked out. Rules represent the hidden nature of things, and can be used to predict or make decisions. Data mining is the new research field against such background, is the cross subjects of many areas, such as statistics, computer science, pattern recognition, artificial intelligence, machine learning, data base.Artificial Immune is an increasingly important area in the field of intellective technology. Biological Immune is a highly complexity and self-adaptive system with capability of learning,memory acquisition,pattern recognition and so on. By simulating and applying the biological immune system, the new computational techniques can solve not only the science but also the engineering problems.The main work and achievements of this paper are as follows.At first, the main characteristic of immune system is generalized, and some necessary explain is pointed out in theory and application research in the paper, from data mining task and object aspect, it respectively expounds the applications of artificial immune system on data mining application in this paper.Second, a new association rule mining algorithm is proposed, which is based on an advanced artificial immune algorithm (AIA).This algorithm mines the association rules of transaction database directly, according to the attributes which users are interested in. Then, the advantages and disadvantages of the new algorithm are analyzed and discussed.At last, this paper presents another immune association rule mining method, and the primary idea of this method is introducing the Immune Clone Selection Theory into the mining process. This method can reduce the cost of scanning the database. At the same time, its confidence converges rapidly. By simulation experiment, we compare the last two algorithms with the traditional Apriori algorithm and association rule mining algorithm based on evolution, study their differences in capability, and analyze their advantages and limitations.