文档介绍：1
Data Mining with an Ant Colony Optimization
Algorithm
Rafael S. Parpinelli1, Heitor S. Lopes1, and Alex A. Freitas2
1 CEFET-PR, CPGEI, Av. Sete de Setembro, 3165, Curitiba - PR, 80230-901, Brazil
2 PUC-PR, ET, Rua Imaculada Conceição, 1155, Curitiba - PR, 80215-901, Brazil.
Abstract – This work proposes an algorithm for data mining called Ant-Miner (Ant Colony-based Data Miner). The
goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the
behavior of real ant colonies and some data mining concepts and principles. pare the performance of Ant-Miner
2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide
evidence that: (a) Ant-Miner petitive 2 with respect to predictive accuracy; and (b) The rule lists
discovered by Ant-Miner are considerably simpler (smaller) than those discovered 2.
Index Terms – Ant Colony Optimization, data mining, knowledge discovery, classification.
I. INTRODUCTION
In essence, the goal of data mining is to extract knowledge from data. Data mining is an inter-disciplinary field,
whose core is at the intersection of machine learning, statistics and databases.
We emphasize that in data mining – unlike for example in classical statistics – the goal is to discover knowledge
that is not only accurate but prehensible for the user [12] [13]. Comprehensibility is important whenever
discovered knowledge will be used for supporting a decision made by a human user. After all, if discovered
knowledge is prehensible for the user, he/she will not be able to interpret and validate it. In this case,
probably the user will not trust enough the discovered knowledge to use it for decision making. This can lead to
wrong decisions.
There are several data mining tasks, including classification, regression, clustering, dependence modeling, etc.
[12]. Each of these tasks can be regarded as a kind of problem to be solved by a dat