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On Clustering Validation Techniques
TLDR
The fundamental concepts of clustering are introduced while it surveys the widely known clustering algorithms in a comparative way and the issues that are under-addressed by the recent algorithms are illustrated.
A foundation for representing and querying moving objects
TLDR
The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL.
Web mining for web personalization
TLDR
This article introduces the modules that comprise a Web personalization system, emphasizing the Web usage mining module, and presents a review of the most common methods that are used as well as technical issues that occur.
Clustering validity assessment: finding the optimal partitioning of a data set
TLDR
A clustering validity procedure, which evaluates the results of clustering algorithms on data sets and defines a validity index, S Dbw, based on well-defined clustering criteria enabling the selection of optimal input parameter values for a clustering algorithm that result in the best partitioning of a data set.
Cluster validity methods: part I
TLDR
This paper presents a review of the clustering validity and methods based on external and internal criteria and discusses the cluster validity approaches based on internal and external criteria.
Spatio-temporal indexing for large multimedia applications
TLDR
This paper presents several spatial, temporal and spatio-temporal relationships of interest, and proposes efficient indexing schemes, based on multidimensional (spatial) data structures, for large multimedia applications that involve thousands of objects.
Clustering validity checking methods: part II
TLDR
The paper illustrates the issues that are under-addressed by the recent approaches to clustering validity checking approaches and proposes the research directions in the field.
Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases
TLDR
This paper proposes a new line of research where moving points and moving regions are viewed as 3-D (2-D space+time) or higher-dimensional entities whose structure and behavior is captured by modeling them as abstract data types.
Text Relatedness Based on a Word Thesaurus
TLDR
Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.
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