  Organizing data into semantically more meaningful is one of the fundamental
modes of understanding and learning. Cluster analysis is a formal study of
methods for understanding and algorithm for learning. K-mean clustering
algorithm is one of the most fundamental and simple clustering algorithms. When
there is no prior knowledge about the distribution of data sets, K-mean is the
first choice for clustering with an initial number of clusters. In this paper a
novel distance metric called Design Specification (DS) distance measure
function is integrated with K-mean clustering algorithm to improve cluster
accuracy. The K-means algorithm with proposed distance measure maximizes the
cluster accuracy to 99.98% at P = 1.525, which is determined through the
iterative procedure. The performance of Design Specification (DS) distance
measure function with K - mean algorithm is compared with the performances of
other standard distance functions such as Euclidian, squared Euclidean, City
Block, and Chebshew similarity measures deployed with K-mean algorithm.The
proposed method is evaluated on the engineering materials database. The
experiments on cluster analysis and the outlier profiling show that these is an
excellent improvement in the performance of the proposed method.
