The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

The research on the stock market prediction has been more popular in recent years.
Numerous researchers tried to predict the immediate future stock prices or indices based on …

Decision tree classification provides a rapid and effective method of categorising datasets.
Many algorithmic methods exist for optimising decision tree structure, although these can be …

Appropriate guidelines for controls in B2C (business-to-consumer) applications (hereafter
B2C controls) should be provided such that these guidelines accomplish efficiency of …

Co-location pattern mining detects sets of features frequently located in close proximity to
each other. This book focuses on data mining for co-location pattern, a valid method for …

Decision trees are among the most popular supervised models due to their interpretability
and knowledge representation resembling human reasoning. Commonly-used decision tree …

This chapter reviews the data reduction problem for instance and feature selection methods
in the context of supervised classification. In the first part, instance and feature selections are …

Modelling land use change is often constrained by imperfect and incomplete data sources.
This paper explores three modelling methodologies and their ability to predict agricultural …

Missing data is one of the major issues in data mining and pattern recognition. The
knowledge contains in attributes with missing data values are important in improving …

Data mining environments produces large volume of data. The large amount of knowledge
contains can be utilized to improve decision-making process of an organization. Large …