@INPROCEEDINGS { Rsds3539,
author = {Kowalczyk, Wojciech}, 
title = {TRANCE: A Tool for Rough data Analysis, Classification and clustEring}, 
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
pages = {269--275}, 
publisher = {Japanese Research Group on Rough Sets}, 
address = {Tokyo, Japan}, 
month = {November}, 
year = 1996, 
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira}, 
isbn = {4-947717-01-7}, 
abstract = {The concept of rough sets involves a partition of the universe into a number of clusters-abstraction classes an indiscernibility relation. In this paper we demonstrate how some properties of these clusters can be combined in a rough data model (RDM), which can be used for data classification, clustering, or scoring. A system that supports building and analyzing such models, TRANCE, has been implemented and evaluated on several data sets. The system can handle huge data sets (millions of records) and the resulting models can be further used for extracting rules or (as input) for other learning algorithms (e.g., neural networks). Basically, the process of constructing RDMs is user driven (the user decides which attributes should be used in the model and how they should be represented.) The main algorithm for calculating RDMs has very low complexity, so the system can also be used for automatic search trough a limited number of alternative models.}, 
keywords = {data analysis, data representation, classification, scoring, rough sets, }, 
}