Attribute weighting in k-nearest neighbor classification

Data mining is the process of getting useful information by analyzing different kind of data. Predictive data mining is used to predict some property of incoming data for example how to classify it. Among many methods that are used for predictive data mining the K-nearest neighbor classification is one of the simplest and easy to use technique. Due to its simplicity small variations are possible with it for the purpose of improving its predictive accuracy. The aim of this thesis was to study attribute weighting techniques and to implement and test some weighting variants in K-nearest neighbor classification. The HEOM distance metric and three values of K (1, 4 and 5) were used in K-nearest neighbor classification. Twelve datasets were selected from the UCI Machine Learning Repository for the analysis. Chi-square attribute weighting was done in order to implement the two weighting variants. One variation was the simple attribute weighting and the other was the class-wise attribute weighting. The evaluation was done by using the leave-one-out technique. The conclusion that can be drawn from the results is that the structure of the dataset (the number and the distribution of the classes) and the value of K (the number of neighbors) have effect on the unweighted and attribute weighted K-nearest neighbor classification. For some datasets weighting is very useful especially for smaller classes, but for some datasets it does not give improvements in the result

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

On Kinds of Indiscernibility in Logic and Metaphysics

Using the Hilbert-Bernays account as a spring-board, we first define four ways in which two objects can be discerned from one another, using the non-logical vocabulary of the language concerned. (These definitions are based on definitions made by Quine and Saunders.) Because of our use of the Hilbert-Bernays account, these definitions are in terms of the syntax of the language. But we also relate our definitions to the idea of permutations on the domain of quantification, and their being symmetries. These relations turn out to be subtle---some natural conjectures about them are false. We will see in particular that the idea of symmetry meshes with a species of indiscernibility that we will call `absolute indiscernibility'. We then report all the logical implications between our four kinds of discernibility. We use these four kinds as a resource for stating four metaphysical theses about identity. Three of these theses articulate two traditional philosophical themes: viz. the principle of the identity of indiscernibles (which will come in two versions), and haecceitism. The fourth is recent. Its most notable feature is that it makes diversity (i.e. non-identity) weaker than what we will call individuality (being an individual): two objects can be distinct but not individuals. For this reason, it has been advocated both for quantum particles and for spacetime points. Finally, we locate this fourth metaphysical thesis in a broader position, which we call structuralism. We conclude with a discussion of the semantics suitable for a structuralist, with particular reference to physical theories as well as elementary model theory.Comment: 55 pages, 21 figures. Forthcoming, after an Appendectomy, in the British Journal for the Philosophy of Scienc

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

The Metaphysics of Resemblance

The topic of this study is the resemblance of individuals. The underlying contention of this dissertation is that the resemblance of individuals is a taxing and challenging philosophical topic. Two main claims are defended in this study to support this contention. The first of these claims is that resemblance is not a binary relation but a monadic multigrade property. The second of these claims is that the metaphysics of resemblance and the metaphysics of properties are distinct, although not independent, philosophical issues. That resemblance is not binary but a monadic multigrade property makes resemblance taxing in at least two ways. First, resemblance is traditionally conceived of as a binary relation and on my account this traditional view is wrong. Second, a metaphysical account of multigrade properties is in itself a challenging issue. That the metaphysics of resemblance and the metaphysics of properties are distinct is motivated by the fact that an answer to the central question of the metaphysics of resemblance, which I identify as the question of whether the resemblance facts are context-relative, is not determined by any positioning on the central debate in the metaphysics of properties: the debate between the realist and the nominalist. Authors engaged in the realist/nominalist debate often address the central question of the metaphysics of resemblance in few words as their interest in resemblance is usually no more than an epiphenomenom of their interest in properties. It is one goal of this study to convince the reader that the central question of the metaphysics of resemblance needs to be addressed with more depth, and that addressing this question is challenging