Data mining is a promising and relatively new technology. Data mining is defined as a process of discovering hidden valuable knowledge by analyzing large amounts of data, which is stored in databases or data warehouse, using various data mining techniques such as machine learning, artificial intelligence (AI) and statistical. Data mining is used regularly in a variety of industries and is continuing to gain in both popularity and acceptance. However, applying data mining methods to complex real-world tasks is far from straight forward and many pitfalls face data mining practitioners. Data mining tools can analyze massive databases to deliver answers to questions such as, which clients most likely to respond to my next promotional mailing, and why? However, most research in the field tends to focus on the algorithmic issues that arise in data mining and ignores the human element and process issues that are often the cause of these pitfalls. Data mining have many advantages but still data mining systems face lot of problems and pitfalls. In this paper approximately 23 research papers were collected concerning various fields in data mining and discussed each and categorized them under the few areas and the trends was interpreted based on the area of research and applications.
Data mining tools, Database, Data warehouse, Knowledge Discovery in Database
