Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Survey of clustering algorithms

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Principles of data mining

In both e-business and e-science, we often need to integrate services across distributed, heterogeneous, dynamic “virtual organizations” formed from the disparate resources within a single enterprise and/or from external resource sharing and service provider relationships. This integration can be technically challenging because of the need to achieve various qualities of service when running on top of different native platforms. We present an Open Grid Services Architecture that addresses these challenges. Building on concepts and technologies from the Grid and Web services communities, this architecture defines a uniform exposed service semantics (the Grid service); defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. The Open Grid Services Architecture also defines, in terms of Web Services Description Language (WSDL) interfaces and associated conventions, mechanisms required for creating and composing sophisticated distributed systems, including lifetime management, change management, and notification. Service bindings can support reliable invocation, authentication, authorization, and delegation, if required. Our presentation complements an earlier foundational article, “The Anatomy of the Grid,” by describing how Grid mechanisms can implement a service-oriented architecture, explaining how Grid functionality can be incorporated into a Web services framework, and illustrating how our architecture can be applied within commercial computing as a basis for distributed system integration—within and across organizational domains. This is a DRAFT document and continues to be revised. The latest version can be found at http://www.globus.org/research/papers/ogsa.pdf. Please send comments to foster@mcs.anl.gov, carl@isi.edu, jnick@us.ibm.com, tuecke@mcs.anl.gov Physiology of the Grid 2

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The Physiology of the Grid An Open Grid Services Architecture for Distributed Systems Integration

The Globus system is intended to achieve a vertically integrated treatment of application, middleware, and net work. A low-level toolkit provides basic mechanisms such as communication, authentication, network information, and data access. These mechanisms are used to con struct various higher level metacomputing services, such as parallel programming tools and schedulers. The long- term goal is to build an adaptive wide area resource environment AWARE, an integrated set of higher level services that enable applications to adapt to heteroge neous and dynamically changing metacomputing environ ments. Preliminary versions of Globus components were deployed successfully as part of the I-WAY networking experiment.

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Globus: a Metacomputing Infrastructure Toolkit

Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective.

A Survey of Clustering Data Mining Techniques

The coming of giga-bit networks makes possible the realization of a single nationwide virtual computer comprised of a variety of geographically distributed high-performance machines and workstations. T o realize the potential that the physical infrastructure provides, software must be developed that is easy to use, supports lar ge degrees of parallelism in applications code, and manages the complexity of the underlying physical system for the user . This paper describes our approach to constructing and exploiting such “metasystems”. Our approach inherits features of earlier work on parallel processing systems and heterogeneous distributed computing systems. In particular , we are building on Mentat, an object-oriented parallel processing system developed at the University of V irginia. This report is a preliminary document. W e expect changes to occur as the architecture and design of the system mature.

Legion: The Next Logical Step Toward a Nationwide Virtual Computer

Clustering partitions a collection of objects into groups called clusters, such that similar objects fall into the same group. Similarity between objects is defined by a distance function satisfying the triangle inequality; this distance function along with the collection of objects describes a distance space. In a distance space, the only operation possible on data objects is the computation of distance between them. All scalable algorithms in the literature assume a special type of distance space, namely a k-dimensional vector space, which allows vector operations on objects. We present two scalable algorithms designed for clustering very large datasets in distance spaces. Our first algorithm BUBBLE is, to our knowledge, the first scalable clustering algorithm for data in a distance space. Our second algorithm BUBBLE-FM improves upon BUBBLE by reducing the number of calls to the distance function, which may be computationally very expensive. Both algorithms make only a single scan over the database while producing high clustering quality. In a detailed experimental evaluation, we study both algorithms in terms of scalability and quality of clustering. We also show results of applying the algorithms to a real life dataset.

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Clustering large datasets in arbitrary metric spaces

We compare the performance of two database selection algorithms reported in the literature. Their performance is compared using a common testbed designed specifically for database selection techniques. The testbed is a decomposition of the TREC/TIPSTER data into 236 subcollections. We present results of a recent investigation of the performance of the CORI algorithm and compare the performance with earlier work that examined the performance of gGlOSS. The databases from our testbed were ranked using both the gGlOSS and CORI techniques and compared to the RBR baseline, a baseline derived from TREC relevance judgements. We examined the degree to which CORI and gGlOSS approximate this baseline. Our results confirm our earlier observation that the gGlOSS Ideal(l) ranks do not estimate relevance-based ranks well. We also find that CORI is a uniformly better estimator of relevance-based ranks than gGlOSS for the test environment used in this study. Part of the advantage of the CORI algorithm can be explained by a strong correlation between gGlOSS and a size-based baseline (SBR). We also find that CORI produces consistently accurate rankings on testbeds ranging from 100--921 sites. However for a given level of recall, search effort appears to scale linearly with the number of databases.

Comparing the performance of database selection algorithms

The proliferation of online information resources increases the importance of effective and efficient distributed searching. Distributed searching is cast in three parts — database selection, query processing, and results merging. In this paper we examine the effect of database selection on retrieval performance. We look at retrieval performance in three different distributed retrieval testbeds and distill some general results. First we find that good database selection can result in better retrieval effectiveness than can be achieved in a centralized database. Second we find that good performance can be achieved when only a few sites are selected and that the performance generally increases as more sites are selected. Finally we find that when database selection is employed, it is not necessary to maintain collection wide information (CWI), e.g. global idf. Local information can be used to achieve superior performance. This means that distributed systems can be engineered with more autonomy and less cooperation. This work suggests that improvements in database selection can lead to broader improvements in retrieval performance, even in centralized (i.e. single database) systems. Given a centralized database and a good selection mechanism, retrieval performance can be improved by decomposing that database conceptually and employing a selection step.

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The impact of database selection on distributed searching

We describe a testbed for database selection techniques and an experiment conducted using this testbed. The testbed is a decomposition of the TREC/TIPSTER data that allows analysis of the data along multiple dimensions, including collection-based and temporal-based analysis. We characterize the subcollections in this testbed in terms of number of documents, queries against which the document,s have been evaluated for relevance, and distribution of relevant documents. We then present initial results from a study conducted using this testbed that examines the effectiveness of the gGlOSS approach to database selection. The databases from our testbed were ranked using the gGl0S.S techniques and compared to the gGlOSS I&l(l) baseline and a baseline derived from TREC relevance judgements. We have examined the degree to which several gGlOSS estimate functions approximate these baselines. Our initial results confirm that the gGZOSS estimators are excellent predictors of the Ideal(Z) ranks but that the Ideal(l) ranks do not estimate relevance-based ranks well.

James C. French

Papers

Legion: The Next Logical Step Toward a Nationwide Virtual Computer

Clustering large datasets in arbitrary metric spaces

Comparing the performance of database selection algorithms

The impact of database selection on distributed searching

Evaluating database selection techniques: a testbed and experiment