Open AccessToward responsive visualization services for scatter/gather browsing
Author(s) -
Ke Weimao,
Mostafa Javed,
Liu Yong
Publication year - 2008
Publication title -
proceedings of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1550-8390
pISSN - 0044-7870
DOI - 10.1002/meet.2008.1450450269
Subject(s) - computer science , cluster analysis , usability , visualization , relevance (law) , constant (computer programming) , time complexity , information retrieval , data mining , algorithm , machine learning , human–computer interaction , political science , law , programming language
As a type of relevance feedback, Scatter/Gather demonstrates an interactive approach to relevance mapping and reinforcement. The Scatter/Gather model, proposed by Cutting, Karger, Pedersen, and Tukey (1992), is well known for its effectiveness in situations where it is difficult to precisely specify a query. However, online clustering on a large data corpus is computationally complex and extremely time consuming. This has prohibited the method's real world application for responsive services. In this paper, we proposed and evaluated a new clustering algorithm called LAIR2, which has linear worst‐case time complexity and constant running time average for Scatter/Gather browsing. Our experiment showed when running on a single processor, the LAIR2 online clustering algorithm is several hundred times faster than a classic parallel algorithm running on multiple processors. The efficiency of the LAIR2 algorithm promises real‐time Scatter/Gather browsing services. We have implemented an online visualization prototype, namely, LAIR2 Scatter/Gather browser, to demonstrate its utility and usability.