Discretization from data streams: Applications to histograms and data mining

• Type: Article in International Conference Proceedings Book
• Date: 2006

Authors

João Gama (Author) FEP; Pinto, C (Author) Other

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 662-667

2006 ACM Symposium on Applied Computing

Dijon, 23 April 2006 through 27 April 2006

Indexing

Scopus - 79 Citations

Other information

Authenticus ID: P-007-GKE

DOI: 10.1145/1141277.1141429

Abstract (EN): In this paper we propose a new method to perform incremental discretization. The basic idea is to perform the task in two layers. The first layer receives the sequence of input data and keeps some, statistics on the data using many more intervals than required. Based on the statistics stored by the first layer, the second layer creates the final discretization. The proposed architecture processes streaming examples in a single scan, in constant time and space even for infinite sequences of examples. We experimentally demonstrate that incremental discretization is able to maintain the performance of learning algorithms in comparison to a batch discretization. The proposed method is much more appropriate in incremental learning, and in problems where data flows continuously, as in most of the recent data mining applications. Copyright 2006 ACM.

Language: English

Type (Professor's evaluation): Scientific