Abstract
Knowledge discovery in databases (KDD) is the process of discovering interesting knowledge from large amounts of data. However, real-world datasets have problems such as incompleteness, redundancy, inconsistency, noise, etc. All these problems affect the performance of data mining algorithms. Thus, preprocessing techniques are essential in allowing knowledge to be extracted from data. This work presents a real world application of knowledge discovery in databases, with the objective of prediction of bankruptcy. For this task fuzzy classification models based on fuzzy clustering are used, which are developed solely from numerical data. This data set has missing values, extreme values and also presents a much smaller bankruptcy class than the not bankruptcy class, which makes it a challenging problem in the scope of KDD.
Original language | English |
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Title of host publication | Proceedings of the 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference, Lisbon, Portugal, July 20-24, 2009 |
Editors | J.P. Carvalho, D. Dubois, U. Kaymak |
Place of Publication | Lisbon |
Publisher | Eusflat |
Pages | 1785-1790 |
ISBN (Print) | 978-989-95079-6-8 |
Publication status | Published - 2009 |
Event | Joint 13th International Fuzzy Systems Association World Congress and 6th European Society of Fuzzy Logic and Technology Conference (IFSA-EUSFLAT 2009), July 20-24, 2009, Lisbon, Portugal - Lisbon, Portugal Duration: 20 Jul 2009 → 24 Jul 2009 |
Conference
Conference | Joint 13th International Fuzzy Systems Association World Congress and 6th European Society of Fuzzy Logic and Technology Conference (IFSA-EUSFLAT 2009), July 20-24, 2009, Lisbon, Portugal |
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Abbreviated title | IFSA-EUSFLAT 2009 |
Country/Territory | Portugal |
City | Lisbon |
Period | 20/07/09 → 24/07/09 |