The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discover...

Recently, applications attracted rampant attention in Epidemiology, Medical Entomology, Bio informatics, and Bio surveillance. Data mining applications is greatly useful to all stake holders in the healthcare industry. Associative Classification (AC) is a branch of data mining, a larger area of scientific study. To build a model for the purpose of prediction, AC is a suitable prediction techniq...

apriori algorithm is the most popular algorithm in association rules mining. one of the problems the apriori algorithm is that the user must specify a minimum support threshold. consider that a user wants to implement the apriori algorithm on a database with millions of transactions; users will not have the necessary knowledge about all the transactions in the database and therefore cannot dete...

This paper is focused on studying how data privacy could be preserved with fuzzy rule bases as interpretable as possible. These fuzzy rule bases are obtained from a data mining strategy based on building a decision tree. The antecedents of each rule produced by these systems contain information about the released variables (quasiidentifier) whereas the consequent contains information only about...

In data mining, the quality of an association rule can be stated by its support and its confidence. This paper investigates support and confidence measures for spatial and spatio-temporal data mining. Using fixed thresholds to determine howmany times a rule that uses proximity is satisfied seems too limited. It allows the traditional definitions of support and confidence, but does not allow to ...

Intrusion Detection is one of the important area of research. Our work has explored the possibility of integrating the fuzzy logic with Data Mining methods using Genetic Algorithms for intrusion detection. The reasons for introducing fuzzy logic is two fold, the first being the involvement of many quantitative features where there is no separation between normal operations and anomalies. Thus f...

Data mining is new but an interdisciplinary field utilizing statistics, machine learning, and other methods. In recent years, fuzzy logic has also been applied to augment data mining. The application of fuzzy logics makes the mining results more understandable and interpretable, apart from being useful and informative. Fuzzy rules are useful to summarize large databases. Several studies are don...

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. I dedicate this book to my grandchildren. Preface The intent of this book is to describe some recent data mining tools that have p...

In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose com...