Improved K-Means clustering technique using distance determination approach
Bhanu Sukhija, Sukhvir Singh · International journal of advanced research in computer science and electronics engineering · 2012
Powerful systems for collecting data and managing it in large databases are in place in all large and mid-range organizations. The value of raw data (collected over a long time) is on the ability to extract high-level information: information useful for decision support, for exploration, and for better understanding of the phenomena generating the data. Traditionally this task of extracting information was done with the help of analysis where one or more analysts with the help of statistical techniques provide summaries and generate reports. Such an approach fails as the volume and dimensionality of the data increase. Hence tools to aid the automation of analysis tasks are becoming a necessity. Thus, data mining was evolved which is “automatic”: extraction of patterns of information from data. Clustering, the grouping of the objects of a database into meaningful subclasses such that similarity of objects in the same group is maximized and similarity of objects in different groups is minimized, is called clustering But my focus is on partitioning methods only , and proposing a new clustering algorithm for automatic discovery of data clusters