Interactive Internet search through automatic clustering (poster abstract)

Roussinov, Dmitri; Tolle, Kristine; Ramsey, Marshall; Chen, Hsinchun

doi:10.1145/312624.312714

Cited by 4 publications

(7 citation statements)

References 5 publications

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“…Our implementation of adaptive search reported in (Roussinov, Tolle, Ramsey, & Chen, 1999), from which we have borrowed some ®gures for illustrative purposes, is described here in more detail.…”

Section: Adaptive Search Implementationmentioning

confidence: 99%

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Information navigation on the web by clustering and summarizing query results

Roussinov

Chen

2001

Information Processing & Management

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We report our experience with a novel approach to interactive information seeking that is grounded in the idea of summarizing query results through automated document clustering. We went through a complete system development and evaluation cycle: designing the algorithms and interface for our prototype, implementing them and testing with human users. Our prototype acted as an intermediate layer between the user and a commercial Internet search engine (AltaVista), thus allowing searches of the signi®cant portion of World Wide Web. In our ®nal evaluation, we processed data from 36 users and concluded that our prototype improved search performance over using the same search engine (AltaVista) directly. We also analyzed eects of various related demographic and task related parameters.

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Section: Adaptive Search Implementationmentioning

confidence: 99%

“…An example of an HTML form generated by AS for the Star Ferry task. (Source: Roussinov et al, 1999. ) What does it cost to ride on the upper deck of the Star Ferry across Hong Kong harbor to Tsimshatsui + \hong kong" + tsimshatsui + \upper deck" + price + \star ferry" + cost -sports…”

Section: How Feedback Was Usedmentioning

confidence: 99%

Information navigation on the web by clustering and summarizing query results

Roussinov

Chen

2001

Information Processing & Management

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“…This section describes the results of the various experiments with CSCA. In order to evaluate the performance, we compared CSCA with other algorithms like K-Means, Fractionation and Buckshot [4].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Cutting et al [4] proposed speeding up the partition-based clustering by using techniques that provide good initial clusters. Two techniques, Buckshot and Fractionation are mentioned.…”

Section: Related Workmentioning

confidence: 99%

A Comparison-Based Soft Clustering Algorithm for Documents

2019

International Journal of Research Studies in Computer Science a

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Data document clustering is an most important tool for searching document such as Web search engines. Clustering data documents enables the accessor to have a good overall view of the information contained in the documents that he has. However, existing clustering algorithms faces from various aspects; complex clustering algorithms (where each document belongs to exactly one cluster) cannot detect the multiple themes of a document, while flexible such as soft clustering algorithms (where each document can belong to multiple clusters) are usually inefficient. We propose CSCA (Comparison-based Soft Clustering), an efficient soft clustering algorithm based on a given similarity measure. CSCA requires only a similarity measure for clustering and uses randomization to help make the clustering efficient. Comparison with existing complex hard clustering algorithms like K-means and its variants shows that CSCA is both effective and efficient.

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“…For instance, [20] discusses an approach called "Adaptive approach" which uses self-organizing maps to cluster documents and also takes feedback from the user and re-clusters the documents. Approaches based on neural networks include one based on an adaptive bilinear retrieval model [25], and a hierarchical model based on fuzzy adaptive resonance theory [17].…”

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confidence: 99%

A modified fuzzy ART for soft document clustering

Kondadadi

Kozma

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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Document clustering is a very useful application in recent days especially with the advent of the World Wide Web. Most of the existing document clustering algorithms either produce clusters of poor quality or are highly computationally expensive. In this paper we propose a document-clustering algorithm, KMART, that uses an unsupervised Fuzzy Adaptive Resonance Theory (Fuzzy-ART) neural network A modified version of the Fuzzy ART is used to enable a document to be in multiple clusters. The number of clusters is determined dynamically. Some experiments are reported to compare the efficiency and execution time of our algorithm with other document-clustering algorithm like Fuzzy c Means. The results show that KMART is both effective and efficient.

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Interactive Internet search through automatic clustering (poster abstract)

Cited by 4 publications

References 5 publications

Information navigation on the web by clustering and summarizing query results

Information navigation on the web by clustering and summarizing query results

A Comparison-Based Soft Clustering Algorithm for Documents

A modified fuzzy ART for soft document clustering

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