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in the context of multi-agent systems. It discusses broadly the connection between DDM and MAS. It provides a high-level survey of DDM, then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem-solving scenarios. It reviews algorithms for distributed clustering, including privacy-preserving ones. It describes challenges for clustering in sensor-network environments, potential shortcomings of the current algorithms, and future work accordingly. It also discusses confidentiality (privacy preservation) and presents a new algorithm for privacy-preserving density-based clustering.

Laxman S., P. S. Sastry, A Survey of Temporal Data Mining, Sadhana, Vol. 31, Part 2, April 2006, pp. 173–198.

Data mining is concerned with analyzing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships that in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning, and databases, the literature is scattered among many different sources. In this article, we present an overview of the techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams. We also describe some recent results regarding statistical analysis of pattern discovery methods.

Mitsa T., Temporal Data Mining, Chapmann & Hall/CRC Press, Boca Raton, FL, 2010.

From basic data-mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, Web-usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purposes of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter.

Pearl, J., Causality: Models, Reasoning and Inference, 2nd edition, Cambridge University Press, Cambridge, UK, 2009.

This book fulfills a long-standing need for a rigorous yet accessible treatise on the mathematics of causal inference. Judea Pearl has done a masterful job of describing the most important approaches and displaying their underlying logical unity. The book deserves to be read by all scientists who use nonexperimental data to study causation, and would serve well as a graduate or advanced undergraduate course text. The book should prove invaluable to researchers in AI, statistics, economics, epidemiology, and philosophy, and, indeed, all those interested in the fundamental notion of causality. It may well prove to be one of the most influential books of the next decade.

Zeitouni K., A Survey of Spatial Data Mining Methods: Databases and Statistics Point of View, in Data Warehousing and Web Engineering, S. Becker, ed., IRM Press, Hershey, PA, 2002.

This chapter reviews the data-mining methods that are combined with GIS for carrying out spatial analysis of geographic data. We will first look at data-mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the