Interesting Pattern Extraction over High Dimensional Data

The tremendous amount of data produced nowadays in various application domains such as molecular biology, time series, spatio temporal patterns can only be fully exploited by efficient and effective data mining tools. In this book, I try to exploit high dimensional datasets using Fuzzy Logic and other approaches. One of the primary data mining task in clustering which is the task of partitioning objects of a data set into distinct groups (clusters) such that two objects from one cluster are similar to each other, whereas two objects from distinct clusters are not.The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of Spatiotemporal Patterns. This area can be defined as efficiently discovering interesting patterns from large data sets. The discovery of hidden periodic patterns in spatiotemporal data could provide unveiling important information to the data analyst. In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals.