Clustering categorical data: an approach based on dynamical systems

Gibson, David; Kleinberg, Jon; Raghavan, Prabhakar

doi:10.1007/s007780050005

Cited by 251 publications

(210 citation statements)

References 21 publications

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“…We find that the notion of topic generalization discussed in Section 7 provides one valuable perspective from which to view the overlapping organization of such communities. In a separate direction, also with Gibson and Raghavan, we have investigated extensions of the present work to the analysis of relational data, and considered a natural, non-linear analogue of spectral heuristics in this setting [29].…”

Section: Resultsmentioning

confidence: 99%

Authoritative Sources in a Hyperlinked Environment

Kleinberg¹

2011

The Structure and Dynamics of Networks

1,436

2,017

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The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of "authoritative" information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of "hub pages" that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.

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Section: Resultsmentioning

confidence: 99%

Authoritative Sources in a Hyperlinked Environment

Kleinberg¹

2011

The Structure and Dynamics of Networks

1,436

2,017

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“…A text document can be represented either in the form of binary data, when we use the presence or absence of a word in the document in order to create a binary vector. In such cases, it is possible to directly use a variety of categorical data clustering algorithms [10,41,43] on the binary representation. A more enhanced representation would include refined weighting methods based on the frequencies of the individual words in the document as well as frequencies of words in an entire collection (e.g., TF-IDF weighting [82]).…”

Section: Document Classificationmentioning

confidence: 99%

“…Traditional methods for clustering have generally focussed on the case of quantitative data [44,71,50,54,108], in which the attributes of the data are numeric. The problem has also been studied for the case of categorical data [10,41,43], in which the attributes may take on nominal values. A broad overview of clustering (as it relates to generic numerical and categorical data) may be found in [50,54].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Text Clustering Algorithms

2012

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Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

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“…Gibson and Kleinberg [24] introduced STIRR, an iterative algorithm based on non-linear dynamic systems for clustering categorical attributes. Ganti et.…”

Section: Related Workmentioning

confidence: 99%

Multinomial Event Model Based Abstraction for Sequence and Text Classification

Kang

Zhang

Silvescu

et al. 2005

Lecture Notes in Computer Science

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In many machine learning applications that deal with sequences, there is a need for learning algorithms that can effectively utilize the hierarchical grouping of words. We introduce Word Taxonomy guided Naive Bayes Learner for the Multinomial Event Model (WTNBL-MN) that exploits word taxonomy to generate compact classifiers, and Word Taxonomy Learner (WTL) for automated construction of word taxonomy from sequence data. WTNBL-MN is a generalization of the Naive Bayes learner for the Multinomial Event Model for learning classifiers from data using word taxonomy. WTL uses hierarchical agglomerative clustering to cluster words based on the distribution of class labels that co-occur with the word counts. Our experimental results on protein localization sequences and Reuters text show that the proposed algorithms can generate Naive Bayes classifiers that are more compact and similar or often more accurate than those produced by standard Naive Bayes learner for the Multinomial Model. Abstract. In many machine learning applications that deal with sequences, there is a need for learning algorithms that can effectively utilize the hierarchical grouping of words. We introduce Word Taxonomy guided Naive Bayes Learner for the Multinomial Event Model (WTNBL-MN) that exploits word taxonomy to generate compact classifiers, and Word Taxonomy Learner (WTL) for automated construction of word taxonomy from sequence data. WTNBL-MN is a generalization of the Naive Bayes learner for the Multinomial Event Model for learning classifiers from data using word taxonomy. WTL uses hierarchical agglomerative clustering to cluster words based on the distribution of class labels that co-occur with the word counts. Our experimental results on protein localization sequences and Reuters text show that the proposed algorithms can generate Naive Bayes classifiers that are more compact and similar or often more accurate than those produced by standard Naive Bayes learner for the Multinomial Model. Disciplines Artificial Intelligence and Robotics

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Clustering categorical data: an approach based on dynamical systems

Cited by 251 publications

References 21 publications

Authoritative Sources in a Hyperlinked Environment

Authoritative Sources in a Hyperlinked Environment

A Survey of Text Clustering Algorithms

Multinomial Event Model Based Abstraction for Sequence and Text Classification

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