Multi-split Decision Tree and Conditional Dispersion

Abstract

Data Mining emerges in response to technological advances and considers the treatment of large amounts of information locked up in databases. The objective of Data Mining is the extraction of new, valid, comprehensible and useful knowledge by the construction of models that seek structural patterns in the data, to ultimately make predictions on future data. The challenge of extracting knowledge from data is an interdisciplinary discipline and draws upon research in statistics, pattern recognition and machine learning among others. Clustering and classification are two methods used in data mining. The key difference between clustering and classification is that clustering is an unsupervised learning technique used to group similar registers whereas classification is a supervised learning technique used to assign predefined class values to registers.