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Four tutorials are featured in ICDM 2007:
Practical Relational Data Community Generation Download PDF
Relational data community generation is concerned with learning community structures from relational data
which involve rich collections of objects linked together in complex relational networks.
Relational data community generation is a recently emerged hot topic in machine learning and data mining research,
and solutions developed in the research hold substantial impacts in many important applications.
A few examples of the important applications include Web community mining, social network mining,
and law enforcement activities. The tutorial "Practical Relational Data Community Generation" by
Bo Long, Zhongfei Zhang, and Philip Yu provides an introduction to the theory, practice,
and open problems in relational data community generation.
Mining for Software Reliability Download PDF
Software reliability is a critical issue: Software is never bug-free, and software bugs keep incurring monetary loss
or even catastrophes. In the pursuit of better reliability, software engineering researchers found that huge amount
of data in various forms can be collected from software systems, and these data, when properly analyzed,
can help improve software reliability. Unfortunately, the huge volume of complex data renders simple analysis
techniques incompetent; consequently, researchers have been resorting to data mining for more effective analysis.
In the past few years, there are many studies on mining for software reliability reported in data mining
as well as software engineering forums. These studies either develop new or apply existing data mining techniques
to tackle reliability problems from different angles. The tutorial "Mining for Software Reliability" by
Chao Liu, Tao Xie, and Jiawei Han presents a comprehensive overview of this area, examines representative studies,
and lays out challenges to data mining researchers. Especially, specific effort is made to let data mining researchers
appreciate the challenges and impact posed by software reliability, and be stimulated to contribute.
Detecting Clusters in Moderate-to-High Dimensional Data: Subspace Clustering,
Pattern-based Clustering, and Correlation Clustering Download PDF
The tutorial "Detecting Clusters in Moderate-to-High Dimensional Data: Subspace Clustering, Pattern-based Clustering,
and Correlation Clustering" by Hans-Peter Kriegel, Peer Kröger, and
Arthur Zimek provides a comprehensive and comparative overview of a broad range
of the state-of-the-art algorithms for finding clusters in
moderate-to-high-dimensional data. It sketches some important applications of the introduced methods,
outlines the general challenges with which these algorithms have to cope, and presents a taxonomy of
the existing approaches. The intended audience of this tutorial includes novice researchers,
advanced experts, as well as practitioners from any application domain dealing with high-dimensional data.
Contrast Data Mining: Methods and ApplicationsDownload PDF
The ability to distinguish, differentiate and contrast between different datasets is a key objective in data mining.
Such an ability can assist domain experts to understand their data, and can help in building classification models.
The tutorial "Contrast data mining: methods and applications" by James Bailey and
Guozhu Dong reviews the principal
techniques for contrasting different types of data, covering the main dataset varieties such as relational, sequence,
and graph forms of data, as well as data cubes. It also focuses on some important real world application areas
that illustrate how mining contrasts is advantageous.
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