期刊名称：Oriental Journal of Computer Science and Technology
印刷版ISSN：0974-6471
出版年度：2012
卷号：5
期号：2
页码：277-281
出版社：Oriental Scientific Publishing Company
摘要：Knowledge Discovery and Data Mining are rapidly evolving areas of research that are at the intersection of several disciplines including statistics, databases, artificial intelligence, visualization and high performance and parallel computing . Data Mining is core part of Knowledge Discovery process (KDD). The KDD process consist of data selection, data cleaning, data transformation, pattern searching ( data mining ) and finding pattern evaluation. Focusing specially, on the definition of data mining, it has been described as “ the task of discovering interesting patterns from large amount of data where the data can be stored in databases, data warehouses or other information repositories”. Thus data mining is extraction of implicit, previously unknown; potentially use for information from the vast amount of data available in the data sets (databases, data warehouses or other information repositories). People in various organizations such as business, science, medicine, academia and government collect such data. The problem is that not enough human analysts are available who are skilled at translating all of the data into knowledge. The development of next generation databases and Management Information System (MIS) has been empowered by data mining, which helps in extraction of hidden useful information and aimed at formulation of knowledge for taking decision by the organization. Thus goal of data mining and knowledge discovery is to turn “data into knowledge”. Data Mining is becoming more widespread every day, because it empowers organizations to uncover profitable patterns and trends from their existing databases. Most of organizations spent millions of dollars to collect megabytes and terabytes of data but are not taking advantage of valuable information stored in it. The tools use different data mining technique and algorithm. The tasks of data mining are distinct because many patterns exist in the large database. All the techniques can be integrated or combined to deal with a complicated problem resides in these large databases. Most of data mining tools employ multiple methods to deal with different kind of data in different application areas. Based on the pattern one is looking for the data-mining task, which can be classified into summarization, classification, clustering, association.
关键词：Data Mining; Management Information System; Artificial Intelligence;Performance Analysis of Data Mining Algorithms to generate frequent itemset at Single and Multiple Levels.
