Building a web‐snippet clustering system based on a mixed clustering method

Abstract

Purpose -Web-snippet clustering has recently attracted a lot of attention as a means to provide users with a succinct overview of relevant results compared with traditional search results. This paper seeks to research the building of a web-snippet clustering system, based on a mixed clustering method. Design/methodology/approach -This paper proposes a mixed clustering method to organise all returned snippets into a hierarchical tree. The method accomplishes two main tasks: one is to construct the cluster labels and the other is to build a hierarchical tree. Findings -Five measures were used to measure the quality of clustering results. Based on the results of the experiments, it was concluded that the performance of the system is better than current commercial and academic systems. Originality/value -A high performance system is presented, based on the clustering method. A divisive hierarchical clustering algorithm is also developed to organise all returned snippets into a hierarchical tree.

A Study of Optimizing Search Engine Results Through User Interaction

“…Users can select different topics produced by clustering, and the system displays personalized search results according to the selected topic. The current well-known academic systems are TagMySearch (SnakeT evolution) [17], [38], WSC [39] and Carrot2 [7], which produce a series of topics and checkboxes based on Web page snippets so users can select topics of interest. Compared with other systems, TagMySearch builds a personalized search system with the ability to select multiple topics at the same time.…”

“…The snippet-oriented clustering first uses the search engine to gather the relevant snippets, then uses the suitable NLP (Natural Language Processing) techniques to remove the noise in the snippet, and finally produces the search topics based on the noise-free snippet. Its purpose is to expect that all the topics correctly represent the needs of the user's search [39]. The advantage of this clustering is the topic is presented in text or graphics, so it has a high reference value for the users.…”

“…They used a flat form rather than a hierarchical form to show the relationship between topics. Chen [39] developed an extended STC algorithm to express hierarchical relationships between topics. The algorithm can present the topic as a sentence rather than as a word.…”