Information navigation on the web by clustering and summarizing query results

Roussinov, Dmitri; Chen, Hsinchun

doi:10.1016/s0306-4573(00)00062-5

Cited by 62 publications

(46 citation statements)

References 33 publications

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“…Our work also share some commonalities with clustering and summarizing web search results [39,22,55,56,51,49,11]. Their methods try to group the search results into clusters and provide easy access and browsing ways for user to get information.…”

Section: Hierarchy Generationmentioning

confidence: 94%

An integrated system for building enterprise taxonomies

Zhang

Liu

et al. 2007

Inf Retrieval

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Although considerable research has been conducted in the field of hierarchical text categorization, little has been done on automatically collecting labeled corpus for building hierarchical taxonomies. In this paper, we propose an automatic method of collecting training samples to build hierarchical taxonomies. In our method, the category node is initially defined by some keywords, the web search engine is then used to construct a small set of labeled documents, and a topic tracking algorithm with keyword-based content normalization is applied to enlarge the training corpus on the basis of the seed documents. We also design a method to check the consistency of the collected corpus. The above steps produce a flat category structure 1 which contains all the categories for building the hierarchical taxonomy.Next, linear discriminant projection approach is utilized to construct more meaningful intermediate levels of hierarchies in the generated flat set of categories. Experimental results show that the training corpus is good enough for statistical classification methods.

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Section: Hierarchy Generationmentioning

confidence: 94%

An integrated system for building enterprise taxonomies

Zhang

Liu

et al. 2007

Inf Retrieval

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“…SOM has been successfully used as a clustering technique in many computing areas such as image analysis, optical patterns, acoustic processing, speech recognition, signal processing, and robotics (Kohonen, 2001). It has also been applied to documents organization (Kaski et al, 1998), database schema (Zhao and Ram, 2004), web search results (Roussinov and Chen, 2001), and financial areas like real estate appraisal, mutual fund portfolio, etc. (Deboeck andKohonen, 1998, Vesanto, 1999).…”

Section: Perceptual Mappingmentioning

confidence: 99%

A Multidimensional Perceptual Map Approach to Project Prioritization and Selection

Zheng¹,

Vaishnavi²

2011

THCI

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Alan Hevner was the accepting Senior Editor. This article was submitted on 5/28/2010 and accepted on 5/9/2011. It was with the authors 180 days for 2 revisions. When prioritizing projects, managers usually have to evaluate multiple attributes (dimensions) of project data. However, these dimensions are usually condensed into one or two indicators in many existing analysis processes. For example, projects are commonly prioritized using a scoring approach: they are evaluated according to predefined categories, which are then aggregated into one or two priority numbers. We argue that aggregated scores may only offer a limited view of project importance. This often leads decision makers to ignore the possible differences masked by the aggregation. Following the design science research paradigm, this paper presents a visual exploration approach based on multi-dimensional perceptual maps. It incorporates human intuition in the process and maintains the multidimensionality of project data as a decision basis for project prioritization and selection. A prototype system based on the approach was developed and qualitatively evaluated by a group of project managers. A qualitative analysis of the data collected shows its utility and usability.

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“…Where the objects being clustered are easy to assess for relevance, such as images or video key-frames, the objects themselves are usually displayed (e.g., Heesch & Rüger, 2004). Where the objects are more complex, clusters will typically have some form of surrogate to label the grouping and aid the searcher's understanding of the grouping (Roussinov & Chen, 2001). Visualizations can be useful but can also create usability problems if there is insufficient support for navigation and searchers' decisions about their own search process (Wiesman, van den Herik, & Hasman, 2004).…”

Section: Clustering Categorization and Browsingmentioning

confidence: 99%

Interactive information retrieval

Ruthven

2008

Annual Review Info Sci & Tec

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Information navigation on the web by clustering and summarizing query results

Cited by 62 publications

References 33 publications

An integrated system for building enterprise taxonomies

An integrated system for building enterprise taxonomies

A Multidimensional Perceptual Map Approach to Project Prioritization and Selection

Interactive information retrieval

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