Chapters authored
Clustering of Time-Series Data
The process of separating groups according to similarities of data is called “clustering.” There are two basic principles: (i) the similarity is the highest within a cluster and (ii) similarity between the clusters is the least. Time-series data are unlabeled data obtained from different periods of a process or from more than one process. These data can be gathered from many different areas that include engineering, science, business, finance, health care, government, and so on. Given the unlabeled time-series data, it usually results in the grouping of the series with similar characteristics. Time-series clustering methods are examined in three main sections: data representation, similarity measure, and clustering algorithm. The scope of this chapter includes the taxonomy of time-series data clustering and the clustering of gene expression data as a case study.
Part of the book: Data Mining
Privacy Preserving Data Mining
Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels.
Part of the book: Data Mining