International Journal of Data Mining Techniques and Applications Vol:02, December 2013, Pages: 275-282 Integrated Intelligent Research (IIR) 275 Certain Investigation on Dynamic Clustering in Dynamic Datamining S.Angel Latha Mary 1 ,Dr.K.R.Shankar Kumar 2 1 Associate Professor, CSE Department, Karpagam college of Engineering,Coimbatore, India 2 Professor, ECE Department, Ranganathan Engineering College xavierangellatha@gmail.com, shanwire@gmail.com Abstract Clustering is the process of grouping a set of objects into classes of similar objects. Dynamic clustering comes in a new research area that is concerned about dataset with dynamic aspects. It requires updates of the clusters whenever new data records are added to the dataset and may result in a change of clustering over time. When there is a continuous update and huge amount of dynamic data, rescan the database is not possible in static data mining. But this is possible in Dynamic data mining process. This dynamic data mining occurs when the derived information is present for the purpose of analysis and the environment is dynamic, i.e. many updates occur. Since this has now been established by most researchers and they will move into solving some of the problems and the research is to concentrate on solving the problem of using data mining dynamic databases. This paper gives some investigation of existing work done in some papers related with dynamic clustering and incremental data clustering. Keywords- Clustering, incremental data clustering dynamic clustering, dynamic data mining, Cluster evaluation, Cluster validity Index. I. INTRODUCTION Clustering is the process of grouping a set of objects into classes of similar objects. This data objects are similar to one another within the same cluster and dissimilar to the objects in the other clusters [1]. Clustering is a challenging problem in scalability, ability to deal with different types of attributes, discovery of cluster with arbitrary shape, domain knowledge to determine input parameters, updating new dataset and visualizing high-dimensional sparse data simultaneously [2]. Dynamic clustering comes in a new research area that is concerned about dataset with dynamic aspects. It requires updates of the clusters whenever new data records are added to the dataset and may result in a change of clustering over time. For example, the bank customer is interested in obtaining his current account status. An economic analyst can receive a lot of new articles every day and he would like to update the relevant associations based on all current articles. Recent developments of clustering systems uses dynamic data which are concerned about the clustering process in dynamically [3][4] . II. DYNAMIC DATA MINING (DDM) Data mining, the process of knowledge discovery in databases (KDD), is concerned with finding patterns in the raw data and finds useful information or to predict trends. Recently, the data are growing with unpredictable rate. Discovering knowledge in these data is a very expensive operation [5]. Running data mining algorithms each time when there is a change in data is a challenging problem. Therefore updating knowledge dynamically will solve these problems. Dynamic data mining is a shift from static analysis to dynamic analysis which discoverers and updates knowledge along with new updated data [3]. Dynamic data mining is very useful to obtain high quality results in the field of time series analysis, telecommunications, mobile networking, nanotechnology, physics, chemistry, biology, health care, sociology and economics [6]. When there is a continuous update and huge amount of dynamic data, rescan the database is not possible in static data mining. But this is possible in Dynamic data mining process [7]. Dynamic data mining applies data mining algorithms on dynamic database. It updates existing set of clusters dynamically. A data warehouse is not updated immediately when insertions and deletions takes place in the databases. These updates are applied to the data warehouse periodically, e.g. each night. This dynamic data mining occurs when the derived information is present for the purpose