A process of partitioning a set of data (or objects) into a set of significant sub-classes, called clusters. Also can be said as unsupervised classification which has no predefined classes. K- Mean is a type of unsupervised learning, which is used when we have any data without defined class or groups. The goal of this algorithm is to find the number of groups in the data, and represented by the variables K1, K2, up till KN. A wavelet based clustering approach for spatial statistics on very huge data. This is a grid based approach which applies wavelet transform in the quantized trait space and then senses the dense section in the transformed space. This paper discusses about the review of the clustering techniques which are observed and used by other numerous researchers for data mining. Further, this paper discusses about the advantages and limitations of the clustering techniques. As based on previous researchers’ contribution that k-mean alone can’t be efficient enough for the increased data set. So with time improved versions were introduced and combining two or more techniques for data mining clustering was practiced. This paper overcame the limitations and found more efficient way for clustering.

Data Mining, Clustring, K-Means Clustering, Wave Clustering

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