K-means clustering Overview in a number of classical data mining techniques the k-means algorithm is one of the most popular technique of unsupervised learning or clustering. that technique can be used with any kind of mining techniques web mining, text mining or any structured data mining. that algorithm is suitable to used for partitioning of data into K number of clusters. therefore that technique is also known as partition based clustering approach. Basic Functioning that technique traditionally need to select k number of initial centroids. these centroids are compared with the remaining data objects in the input data samples. in order to compare the data objects the distance functions are used such asÂ euclidean distance. Basics of clustering Clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. This clustering analysis allows an object not to be part of a cluster, or strictly belong to it, calling this type of grouping hard partitioning. In the other hand, soft partitioning states that every object belongs to a cluster in a determined…