Successful completion of this course demonstrate your achievement of the following learning outcomes for the MS-DS program: Identify the core functionalities of data modeling in the data mining ...

Classification is often confused with another data mining technique, clustering. As we’ll see later on, both techniques offer stark differences for businesses. Outlier and Anomaly Detection ...

A k-means-type algorithm is proposed for efficiently clustering data constrained to lie on the surface of a p-dimensional unit sphere, or data that are mean-zero-unit-variance standardized ...

Brief Description of Course Content Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to ...

Statistica Sinica, Vol. 12, No. 1, A Special Issue on Bioinformatics (January 2002), pp. 241-262 (22 pages) Many clustering algorithms have been used to analyze microarray gene expression data. Given ...

Clustering is a commonly considered data mining problem in the text domains. The problem finds numerous applications in customer segmentation, ...