Cluster analysis for data mining and system identification

Meer over het boek

Dataclusteringisacommontechniqueforstatisticaldataanalysis, utilized across various fields such as machine learning, data mining, pattern recognition, image analysis, and bioinformatics. Clustering involves classifying similar objects into groups, partitioning a data set into subsets (clusters) where data in each subset ideally share a common trait, often based on proximity defined by a distance measure. This work aims to demonstrate that advanced fuzzy clustering algorithms can be applied not only for data partitioning but also for visualization, regression, classification, and time-series analysis. Thus, fuzzy cluster analysis serves as an effective approach for addressing complex data mining and system identification challenges. In recent years, the volume of stored data has surged across nearly all aspects of life. A survey from Berkeley University highlighted that data produced in 2002 and stored in various media amounted to about 5 exabytes. For context, if the 17 million volumes of the Library of Congress were digitized, they would total around 136 terabytes—indicating that 5 exabytes equals approximately 37,000 Library of Congresses. This translates to each person on Earth generating roughly 800 megabytes of data annually, a stark contrast to Shakespeare’s entire body of work, which could fit into just 5 megabytes.