Scatter/Gather: a cluster-based approach to browsing large document collections

Cutting, Douglass R.; Karger, David R.; Pedersen, Jan O.; Tukey, John W.

doi:10.1145/133160.133214

Cited by 961 publications

(661 citation statements)

References 7 publications

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“…A classical example of this is the Scatter/Gather method [25], which provides a systematic browsing technique with the use of clustered organization of the document collection.…”

Section: Document Organization and Browsingmentioning

confidence: 99%

“…The most well known similarity function which is used commonly in the text domain is the cosine similarity function. Let [25]. Then, the cosine similarity between the two documents is defined as follows:…”

Section: Distance-based Clustering Algorithmsmentioning

confidence: 99%

“…One of the advantages of the k-means method over the k-medoids method is that it requires an extremely small number of iterations in order to converge. Observations from [25,83] seem to suggest that for many large data sets, it is sufficient to use 5 or less iterations for an effective clustering. The main disadvantage of the k-means method is that it is still quite sensitive to the initial set of seeds picked during the clustering.…”

Section: K-means Clustering Algorithmsmentioning

confidence: 99%

“…For example, another lightweight clustering method such as an agglomerative clustering technique can be used in order to decide the initial set of seeds. This is at the core of the method discussed in [25] for effective document clustering. We will discuss this method in detail in the next subsection.…”

Section: K-means Clustering Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

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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“…A classical example of this is the Scatter/Gather method [25], which provides a systematic browsing technique with the use of clustered organization of the document collection.…”

Section: Document Organization and Browsingmentioning

confidence: 99%

Section: Distance-based Clustering Algorithmsmentioning

confidence: 99%

Section: K-means Clustering Algorithmsmentioning

confidence: 99%

Section: K-means Clustering Algorithmsmentioning

confidence: 99%

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A Survey of Text Clustering Algorithms

2012

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“…The visualization of unstructured data, such as documents, webpages, and tags, is studied in several papers [14,17,13,15]. For structured data, Catarci et al [12] surveyed a large number of systems that visualize relational data.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Heterogeneous Data

Cammarano

Dong

Chan

et al. 2007

IEEE Trans. Visual. Comput. Graphics

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Abstract-Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template.

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Interaction in information retrieval: Trends over time

Savage‐Knepshield

Belkin

1999

J. Am. Soc. Inf. Sci.

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This review examines a sample of interactive information retrieval (IR) systems within the framework of a design challenge set forth by John Bennett in 1971. Bennett directed his challenge to a group of information scientists who attended The User Interface for Interactive Search of Bibliographic Data Bases Workshop. During the workshop, participants exchanged their insights and experiences on various aspects of user interface requirements for interactive IR systems and discussed system design features that could result in the design of more effective user interfaces for information retrieval. The main goals of this review are to characterize interactive IR design trends over time, examine the extent to which IR systems have responded to Bennett's design challenge, and to suggest potential future research directions and challenges.

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Scatter/Gather: a cluster-based approach to browsing large document collections

Abstract: Documentclustering has not been well received as an information retrieval tool.

Cited by 961 publications

References 7 publications

A Survey of Text Clustering Algorithms

A Survey of Text Clustering Algorithms

Visualization of Heterogeneous Data

Interaction in information retrieval: Trends over time

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