World Scientific, 2005. — 345 p.Data mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of knowledge discovery in databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity.
One of the most practical approaches in data mining is to use induction algorithms for constructing a model by generalizing from given data. The induced model describes and explains phenomena which are hidden in the data. Given the recent growth of the field as well as its long history, it is not surprising that several mature approaches to induction are now available to the practitioner. However according to the “no free lunch” theorem, there is no single approach that outperforms all others in all possible domains. Evidently, in the presence of a vast repertoire of techniques and the complexity and diversity of the explored domains, the main challenge today in data mining is to know how to utilize this repertoire in order to achieve maximum reliability, comprehensibility and complexity.
Multiple classifiers methodology is considered an effective way of overcoming this challenge. The basic idea is to build a model by integrating multiple models. Researchers distinguish between two multiple classifier methodologies: ensemble methodology and decomposition methodology. Ensemble methodology combines a set of models, each of which solves the same original task. Decomposition methodology breaks down the classification task into several manageable classification tasks, enabling each inducer to solve a different task.
This book focuses on decomposition in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools. The various elements are then joined together to solve the initial problem.
The benefits of decomposition methodology in data mining include: increased performance (classification accuracy); conceptual simplification of the problem; enhanced feasibility for huge databases; clearer and more comprehensible results; reduced runtime by solving smaller problems and by using parallel/distributed computation; and the opportunity of using different solution techniques for individual sub-problems. These features are discussed in the book.
Obviously the most essential question that decomposition methodology should be able to answer is whether a given classification problem should be decomposed and in what manner. The main theory presented in this book is that the decomposition can be achieved by recursively performing a sequence of single, elementary decompositions. The book introduces several fundamental and elementary decomposition methods, namely: Attribute Decomposition, Space Decomposition, Sample Decomposition, Function Decomposition, and Concept Decomposition. We propose a unifying framework for using these methods in real applications.
The book shows that the decomposition methods developed here extend the envelope of problems that data mining can efficiently solve. These methods also enhance the comprehensibility of the results that emerge and suggest more efficient implementation of knowledge discovery conclusions.Knowledge Discovery and Data Mining: Concepts and Fundamental Aspects.
Decision Trees.
Clustering Methods.
Ensemble Methods.
Elementary Decomposition Framework.
Feature Set Decomposition.
Space Decomposition.
Sample Decomposition.
Function Decomposition.
Concept Aggregation.
A Meta-Classification for Decomposition Methodology.
