Basic-K means, Bisect K-means and Agglomerative Hierarchical Clustering algorithms

K-means clustering techniques can cluster the data into K clusters depending on the attributes of the data

The module used

• Sum of Squared Error ( SSE ) which is based on Euclidean distance
• can cluster with or without normalizing the data
• basic k-means, bisect k-means and Agglomerative Hierachical Clustering and compared result
• could allow proximity options for either single Link, Complete Link and Group Average
• can measure performance based on Silhouette Coefficient and Rand Statistics on the data sets

Programming Language : Java in Model View Controller design pattern

Module was developed only using standard Java libraries under JDK 8 and not by the use of any predefined data mining libraries