Ant Colonies and Data Mining

Ant Colonies and Data Mining

Miroslav Bursa (Czech Technical University in Prague, Czech Republic) and Lenka Lhotska (Czech Technical University in Prague, Czech Republic)
DOI: 10.4018/978-1-60566-310-4.ch019
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The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a constantly growing area of current research. Medicine, market, trade, and meteorology belong to the numerous fields that benefit of its techniques. First an introduction into data mining and cluster validation techniques is presented, followed by a review of ant-inspired concepts and applications. The chapter provides a reasonably deep insight into the most successful ant colony and swarm intelligence concepts, their paradigms and application. The authors present discussion, evaluation and comparison of these techniques. Important applications and results recently achieved are provided. Finally, new and prospective future directions in this area are outlined and discussed.
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This chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of data clustering in biomedical data processing. Clustering is a constantly growing area of current research. Medicine, market, trade, and meteorology are some of the numerous fields that benefit of its techniques.

The objective of this chapter is to introduce the methods for clustering together with the methods for evaluation of different clusterings. It presents the fundamentals of ant inspired methods, followed by a compact review of the basic ant clustering models together with the most successful variations and modifications. In the second part, it presents the application of ant-colony clustering in biomedical data processing.

In the last two decades, many advances in computer sciences have been based on the observation and emulation of processes of the natural world.

The coordination of an ant colony is of local nature, composed mainly of indirect communication through pheromone (also known as stigmergy; the term has been introduced by Grassé et al. (1959)), although direct interaction communication from ant to ant (in the form of antennation) and direct communication have also been observed (Trianni, Labella, & Dorigo, 2004). In studying these paradigms, we have high chance to discover inspiring concepts for many successful metaheuristics. More information on the ant colony metaphors can be found in the section Ant Colony Optimization.

The author himself specializes on the use of such kind of methods in the area of biomedical data processing. The application is described in the section Applications.

The chapter is organized as follows: First, an introduction to data mining and clustering is presented together with a brief survey on ant colony inspired methods in clustering. Then, a natural background of applied methods is presented. It summarizes the most important properties of ant colonies that served as an inspiration source for many algorithms that are described in the following part. The next section describes the most successful methods in data clustering: first, the pioneering ant-inspired clustering algorithms are described followed by the evolution of further ant-inspired algorithms for clustering. Finally, applications of the algorithms and paradigms published by the author are presented, followed by conclusion and future directions. At the end, relevant literature has been carefully selected to provide the reader with additional resources containing the state-of-the-art information in the area.

Key Terms in this Chapter

Stigmergy: Indirect communication in social insect communities via changing the environment. The term has been introduced by Grasse et al.

Cluster Validity Measures: Indices to measure the quality of clustering obtained. When the correct classification is known, SSE measure (sum of square of errors) or accuracy can be used. If it is not known, other measures can used, such as Davies-Bouldin index, Dunn Index, Silhouette index, Mutual information, etc.

Data Mining: Important branch in industry and market, retrieving important information from a huge amount of data. It is usually considered with huge amount of heterogeneous data, where the use of computers is inevitable.

Pheromone: Chemical substance deposited by ants to mark their path and the importance of prey found. The ants are nearly blind, they mainly sense the amount of the pheromone deposited.

Clustering: Automated process for grouping similar data together. It minimizes the intra-cluster distance while maximizing the inter-cluster distance. It is a multi-objective optimization, some instances are NP hard (when the number of classes is higher than two).

Bioinspired Informatics: Scientific branch with industry applicability that tries to reproduce the mental processes of the brain and biogenesis respectively, in a computer environment. These methods are used to solve NP-hard problems with exponential complexity.

Social Insect: Insect which is not able to survive on its own; however in colonies it provides astonishing solutions. Usually, the behavior of an individual is very simple, however, on the colony level it shows interesting behavior (traverse open spaces, determine shortest path, etc.).

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Editorial Advisory Board
Table of Contents
Lipo Wang
Hongwei Mo
Chapter 1
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Chapter 2
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The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to... Sample PDF
Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications
Chapter 3
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Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection... Sample PDF
An Artificial Immune Dynamical System for Optimization
Chapter 4
Malgorzata Lucinska, Slawomir T. Wierzchon
Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they... Sample PDF
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
Chapter 5
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Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high... Sample PDF
Applications of Artificial Immune Systems in Agents
Chapter 6
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Inspired from the robust control principle, a robust scheduling method is proposed to solve uncertain scheduling problems. The uncertain scheduling... Sample PDF
An Immune Algorithm Based Robust Scheduling Methods
Chapter 7
Fabio Freschi, Maurizio Repetto
The increasing cost of energy and the introduction of micro-generation facilities and the changes in energy production systems require new... Sample PDF
Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems
Chapter 8
Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large... Sample PDF
Applying the Immunological Network Concept to Clustering Document Collections
Chapter 9
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Feature Selection Based on Clonal Selection Algorithm: Evaluation and Application
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Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet
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Static Web immune system is an important applicatiion of artificial immune system, and it is also a good platform to develop new immune computing... Sample PDF
A Static Web Immune System and Its Robustness Analysis
Chapter 12
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Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal... Sample PDF
Immunocomputing for Spatio-Temporal Forecast
Chapter 13
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Research of Immune Controllers
Chapter 14
Xiaojun Bi
In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete... Sample PDF
Immune Programming Applications in Image Segmentation
Chapter 15
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A Hardware Immune System for MC8051 IP Core
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Mark Burgin, Eugene Eberbach
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On Foundations of Evolutionary Computation: An Evolutionary Automata Approach
Chapter 17
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Chapter 18
Konstantinos Konstantinidis, Georgios Ch. Sirakoulis, Ioannis Andreadis
The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony... Sample PDF
Ant Colony Optimization for Use in Content Based Image Retrieval
Chapter 19
Miroslav Bursa, Lenka Lhotska
The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a... Sample PDF
Ant Colonies and Data Mining
Chapter 20
Bo-Suk Yang
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Chapter 21
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