Jiawei Han, Jae-Gil Lee, Hector Gonzalez, Xiaolei Li
Department of Computer Science, University of Illinois at Urbana-Champaign

Abstract
With the wide availability of satellite, RFID, GPS, sensor, wireless, and video technologies, moving-object data has been collected in massive scale and is becoming increasingly rich, complex, and ubiquitous. There is an imminent need for scalable and flexible data analysis over moving-object information; and thus mining moving-object data has become one of major challenges in data mining. There have been considerable research efforts on data mining for RFID, trajectory, and traffic data sets. However, there has been no systematic tutorial on knowledge discovery from such moving-object data sets. This tutorial presents a comprehensive, organized, and state-of-the-art survey on methodologies and algorithms on analyzing different kinds of moving-object data sets, with an emphasis on several important mining tasks: clustering, classification, outlier analysis, and multidimensional analysis. Besides a thorough survey of the recent research work on this topic, we also show how real-world applications can benefit from data mining of RFID, trajectory, and traffic data sets. The tutorial consists of three parts: (1) RFID data mining, (2) trajectory data mining, and (3) traffic data mining. In the first part, warehousing, cleaning, and flow mining for RFID data are explored. In the second part, pattern mining, clustering, classification, and outlier detection for trajectory data are explored. In the third part, route discovery, destination prediction, and hot-route or outlier detection for traffic data are explored. This tutorial is prepared for data mining, database, and machine learning researchers who are interested in moving-object data.

Jennifer Neville, Purdue University
Foster Provost, New York University

Abstract
Recently there has been a surge of interest in methods for analyzing complex social networks: from communication networks, to friendship networks, to professional and organizational networks. The dependencies among linked entities in the networks present an opportunity to improve inference about properties of individuals, as birds of a feather do indeed flock together. For example, when deciding how to market a product to people in MySpace or Facebook, it may be helpful to consider whether a person's friends are likely to purchase the product.

This tutorial will explore the unique opportunities and challenges for modeling social network data. We will begin with a description of the problem setting, including examples of various applications of social network mining (e.g., marketing, fraud detection). We will then present a number of characteristics of social network data that differentiate it from traditional inference and learning settings, and outline the resulting opportunities for significantly improved inference and learning. We will discuss specific techniques for capitalizing on each of the opportunities in statistical models, and outline both methodological issues and potential modeling pathologies that are unique to network data. We will give links to the recent literature to guide study, and present results demonstrating the effectiveness of the techniques.

Prerequisites: The tutorial assumes a basic knowledge of AI-style inference and machine learning, equivalent to an introductory graduate or advanced undergraduate class.

Jian Pei, Simon Fraser University, Canada
Ming Hua, Simon Fraser University, Canada
Yufei Tao, The Chinese University of Hong Kong
Xuemin Lin, The University of New South Wales, Australia

Abstract
Uncertain data are inherent in some important applications, such as environmental surveillance, market analysis, and quantitative economics research. Uncertain data in those applications are generally caused by factors like data randomness and incompleteness, limitations of measuring equipment, delayed data updates, etc. Due to the importance of those applications and the rapidly increasing amount of uncertain data collected and accumulated, analyzing and mining large collections of uncertain data have become an important task and attracted more and more interest from the data mining community. In this tutorial, we will give a systematic survey on the motivations/applications, the problems, the challenges, the fundamental principles and the state-of-the-art methods of mining uncertain and probabilistic data. We will motivate the survey with several interesting practical applications of uncertain data analysis. To set the stage, we will discuss two major models for uncertain and probabilistic data briefly. We will cover several important data mining tasks on uncertain data, including clustering, classification, frequent pattern mining and online analytical processing (OLAP). For each task, we will analyze the challenges posed by uncertain and probabilistic data and the state-of-the-art solutions.

Hans-Peter Kriegel, Peer Kröger, and Arthur Zimek
Institute for Informatics, Ludwig-Maximilians-Universitat Munchen, Germany

Abstract
This tutorial provides a comprehensive and comparative overview of a broad range of state-of-the-art algorithms for finding clusters in moderate-to-high-dimensional data. It sketches important applications of the introduced methods, outlines the general challenges these algorithms have to cope with, and presents a taxonomy of existing approaches. In addition, relationships between the algorithmic approaches of each category of the taxonomy are discussed. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with high-dimensional data.

Huan Liu, Arizona State University
Nitin Agarwal, Arizona State University

Abstract
The objective of this tutorial is to give a comprehensive overview of the techniques, applications, and research issues in the blogosphere. Weblogs, or Blogs, have facilitated people to express their thoughts, voice their opinions, and share their experiences and ideas. Individuals experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their niche needs will be met through online interactions. Its open standards and low barrier to publication have transformed information consumers to producers. This has created a plethora of open-source intelligence, or "collective wisdom" that acts as the storehouse of overwhelming amounts of knowledge about the members, their environment and the symbiosis between them. Nonetheless, vast amounts of this knowledge still remain to be discovered and exploited in its most suitable way. In this tutorial, we introduce current and state-of-the-art research issues, review some key elements of research such as tools and methodologies in Blogosphere, and present a case study of identifying the influential bloggers in a community to exemplify the integration of some major aspects discussed in this tutorial.

Karsten Borgwardt, University of Cambridge
Xifeng Yan, IBM T.J. Watson Research Center New York

Abstract Social and biological networks have led to a huge interest in data analysis on graphs. Various groups within the KDD community have begun to study the task of data mining on graphs, including researchers from database-oriented graph mining, and researchers from kernel machine learning. Their approaches are often complementary, and we feel that exciting research problems and techniques can be discovered by exploring the link between these different approaches to graph mining.

This tutorial presents a comprehensive overview of the techniques developed in graph mining and graph kernels and examines the connection between them. The goal of this tutorial is i) to introduce newcomers to the field of graph mining, ii) to introduce people with database background to graph mining using kernel machines, iii) to introduce people with machine learning background to database-oriented graph mining, and iv) to present exciting research problems at the interface of both fields.

Ronen Feldman, Hebrew University
Lyle Ungar, University of Pennsylvania

Abstract
The information age has made it easy to store large amounts of data. The proliferation of documents available on the Web, on corporate intranets, on news wires, and elsewhere is overwhelming. However, while the amount of data available to us is constantly increasing, our ability to absorb and process this information remains constant. Search engines only exacerbate the problem by making more and more documents available in a matter of a few key strokes. Text Mining is an exciting research area that tries to solve the information overload problem by using techniques from data mining, machine learning, NLP, IR and knowledge management. Text Mining involves the preprocessing of document collections (text categorization, information extraction, term extraction), the storage of the intermediate representations, the techniques to analyze these intermediate representations (distribution analysis, clustering, trend analysis, association rules etc) and visualization of the results. In this tutorial we will present the general theory of Text Mining and will demonstrate several systems that use these principles to enable interactive exploration of large textual collections. We will present a general architecture for text mining and will outline the algorithms and data structures behind the systems. Special emphasis will be given to lessons learned from years of experience in developing real world text mining systems. The Tutorial will cover the state of the art in this rapidly growing area of research. Several real world applications of text mining will be presented.