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A Study On Density-Based And Other Cluster Analysis Techniques In Data Mining


There is a lot of data in information indus-try that is useless unless it is converted to useful information. This process involves analyzing this bulk data and then extract-ing relevant information from it. This ex-traction of data is referred to as data min-ing from that particular resource. Other processes involved are Data Cleaning, Da-ta Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. After these processes are complete the data is said to be mined into useful information. Data mining finds its application in Fraud Detection, Market Analysis, Production Control, Science Ex-ploration, etc. A group of objects belong-ing to a same class is referred as a cluster, by partitioning the set of data into groups pertaining similar characteristics. Cluster-ing methods can be classified into Parti-tioning Method, Hierarchical Method, Density-based Method, Grid-Based Meth-od, Model-Based Method and Constraint-based Method. Here we will focus on Den-sity-based method, where every data point within a given cluster, the radius of a giv-en cluster has to contain at least a mini-mum number of points.

Key Words

Density, Cluster, Data-mining, Classifica-tion, hard clustering, soft clustering, parti-tion algorithm, Hierarchical Method, Densi-ty-based Method, Grid-Based Method, Model-Based Method and Constraint-based Method, outlier


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