A Novel Approach for High Dimensional Data Clustering
Ali Alijamaat, Madjid Khalilian, Norwati Mustapha · 2010
Clustering is considered as the most important unsupervised learning problem. It aims to find some structure in a collection of unlabeled data. Dealing with a large quantity of data items can be problematic because of time complexity. On the other hand high dimensional data is a challenge arena in data clustering e.g. time series data. Novel algorithms are needed to be robust, scalable, efficient and accurate to cluster of these kinds of data. In this study we proposed a two stages algorithm base on K-Means to achieve our objective.