The primary difference between clustering and classification is that classification works with predefined classes. Clustering does not use pre-labeled data or training sets.

Incomplete data affects classification accuracy and hinders effective data mining. The following techniques are effective for working with incomplete data. The ISOM-DH model handles incomplete ...

Same as CSCA 5512Same as CSCA 5512 Specialization: Data Mining Foundations and Practice Instructor: Dr. Qin (Christine) Lv, Associate Professor of Computer Science Prior knowledge needed: Familiarity ...

Clustering and classification represent the opportunity to apply categorical reasoning to vast data contexts we would otherwise find overwhelming.

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 ...

We also propose an approach for clustering sequencing data using a new dissimilarity measure that is based upon the Poisson model. We demonstrate the performances of these approaches in a simulation ...

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

The thesis presents the PULS media monitoring system and describes several news mining tasks, namely document clustering, multi-label news classification and text polarity detection. For each task, we ...