Application of K-means algorithm in data mining

It is an algorithm to classify or to group your objects based on attributes/features into K number of classified clusters based on the similarity of some attributes. Here K is positive integer number. The types of diabetes disorder symptoms are grouped based on the category of diabetes. The grouping is done by minimizing the sum of squares of distance between data and the corresponding cluster centroid. Thus, the purpose of K-mean clustering is to classify the data. Here the attributes are significant factors causing diabetes such as body mass index, diabetes pedigree function, Plasma glucose concentration in saliva and age. These factors must be grouped based on acquiring a type of diabetes or not. The acquired factor k. partitions the data into classes with high intra-class similarity or low inter-class similarity. An algorithm starts with a random solution, and iteratively makes small changes to the solution, each time improving it a little. When the algorithm cannot see any improvement anymore, it terminates. Ideally, at that point the current solution is close to optimal solution. The k-means algorithm is a simple iterative method to partition a given dataset into a user specified number of clusters, k. • Here it is tested with a small cluster of symptoms and types of diabetes disorder. One needs to find a suitable stopping criterion for large dataset in medical diagnosis. Here it groups the type-i and type-ii diabetes in one group which is commonly present in most population also mody and gestational diabetes which occur in selective group in another cluster. K-means remains the most widely used partition clustering algorithm in practice. The algorithm is simple, easily understandable and reasonably scalable.