Data Clustering and Techniques with Weights Over Data Stream

Abstract

Data mining refers to extract and identify useful information from large sets of data. This term is really a misnomer. Thus, data mining should be named as knowledge mining which rely stress on mining from vast sets of data . An enormous quantity of data is present in the information industry. This data is meaningless until it is converted into useful form of information or help the industries in their business. It is essential to analyze this plenty of data and extract the valuable information from it. In data mining, extraction of information is not only the process to be performed it also involves various other process such as cleaning, integration, data transformation, data mining, pattern evaluation and presentation. When all these processes are completed one will be able to use this valuable information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration, etc.(Dhaka et al.,2018)

This paper introduces the significance use of data mining techniques such as clustering, association-rules, sequential pattern, statistics analysis, characteristics rules and so on can be used to find out the useful knowledge. Finally, various tools has been explained in this paper.