Data mining is the process of extracting potentially useful information from a data set. Clustering is a popular data mining technique which is intended to help the user discover and understand the structure or grouping of the data in the set according to a certain similarity measure. Clustering algorithms usually employ a distance metric e.g., Euclidean or a similarity measure in order to partition the database so that the data points in each partition are more similar than points in different partitions. The commonly used Euclidean distance, while computationally simple, requires similar objects to have close values in all dimensions. However, with the high-dimensional data commonly encountered nowadays, the concept of similarity between objects in the full-dimensional space is often invalid and generally not helpful.