Data mining is the technique by which one can extract efficient and effective data from huge amount of raw data. There are various techniques for extracting useful data. Attribute Selection is one of the effective methods for this operation. To obtain useful data from enormous amount of data is not a simple task. It contains several phases like pre-processing, classification, analysis etc. Attribute Selection is an important topic in Data Mining, as it is the effective way to reduce dimensionality, to remove irrelevant data, to remove redundant data, & to increase accuracy of the data. It is the process of identifying a subset of the most useful attributes that produces attuned results as the original entire set of attribute. In last few years, there were different techniques for attribute selection. These techniques were judged on two measures i. e. efficiency is time to find the clusters and effectiveness is quality of subset attributes. Some of the techniques are Wrapper approach, Filter Approach, Relief Algorithm, Distributional clustering etc. But each of one having some drawbacks like unable to handle large volumes of data, computational complexity, accuracy is not guaranteed, difficult to evaluate and redundancy detection etc. To overcome some of these problems in attribute selection method this paper proposes technique that aims to provide an effective clustering based attribute selection method for high dimensional data. Initially this technique removes irrelevant attributes depending on some threshold value. Afterwards, using Kruskal's algorithm minimum spanning tree is constructed from these attributes. From that tree some representative attributes are selected by partitioning. This is nothing but the final set of attributes.

Attribute Selection Clustering Data Mining Graph-Based Clustering Minimum Spanning Tree.