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Karan Singal

dblp:68/2069 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 75% Data mining · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › clustering
hierarchical clustering
0.012004
A hierarchical monothetic document clustering algorithm for summarization and browsing search results · WWW 2004
Information retrieval › search interfaces
search result organization
0.012004
A hierarchical monothetic document clustering algorithm for summarization and browsing search results · WWW 2004
Information retrieval › text summarization
search result summarization
0.012004
A hierarchical monothetic document clustering algorithm for summarization and browsing search results · WWW 2004
Information retrieval › document organization
topic hierarchy
0.012004
A hierarchical monothetic document clustering algorithm for summarization and browsing search results · WWW 2004

Methods — techniques the papers use, named apart from their topics

user study · 0.0monothetic clustering · 0.0
YearPublicationVenuePosition
2004 A hierarchical monothetic document clustering algorithm for summarization and browsing search results
abstract
Organizing Web search results into a hierarchy of topics and sub-topics facilitates browsing the collection and locating results of interest. In this paper, we propose a new hierarchical monothetic clustering algorithm to build a topic hierarchy for a collection of search results retrieved in response to a query. At every level of the hierarchy, the new algorithm progressively identifies topics in a way that maximizes the coverage while maintaining distinctiveness of the topics. We refer the proposed algorithm to as DisCover. Evaluating the quality of a topic hierarchy is a non-trivial task, the ultimate test being user judgment. We use several objective measures such as coverage and reach time for an empirical comparison of the proposed algorithm with two other monothetic clustering algorithms to demonstrate its superiority. Even though our algorithm is slightly more computationally intensive than one of the algorithms, it generates better hierarchies. Our user studies also show that the proposed algorithm is superior to the other algorithms as a summarizing and browsing tool.
Krishna Kummamuru, Rohit Lotlikar, Shourya Roy, Karan Singal, Raghu Krishnapuram
WWW4