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Titlebook: Clustering and Information Retrieval; Weili Wu,Hui Xiong,Shashi Shekhar Book 2004 Kluwer Academic Publishers 2004 algorithms.architecture.

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發(fā)表于 2025-3-21 16:36:13 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Clustering and Information Retrieval
編輯Weili Wu,Hui Xiong,Shashi Shekhar
視頻videohttp://file.papertrans.cn/229/228551/228551.mp4
叢書名稱Network Theory and Applications
圖書封面Titlebook: Clustering and Information Retrieval;  Weili Wu,Hui Xiong,Shashi Shekhar Book 2004 Kluwer Academic Publishers 2004 algorithms.architecture.
描述Clustering is an important technique for discovering relatively dense sub-regions or sub-spaces of a multi-dimension data distribution. Clus- tering has been used in information retrieval for many different purposes, such as query expansion, document grouping, document indexing, and visualization of search results. In this book, we address issues of cluster- ing algorithms, evaluation methodologies, applications, and architectures for information retrieval. The first two chapters discuss clustering algorithms. The chapter from Baeza-Yates et al. describes a clustering method for a general metric space which is a common model of data relevant to information retrieval. The chapter by Guha, Rastogi, and Shim presents a survey as well as detailed discussion of two clustering algorithms: CURE and ROCK for numeric data and categorical data respectively. Evaluation methodologies are addressed in the next two chapters. Ertoz et al. demonstrate the use of text retrieval benchmarks, such as TRECS, to evaluate clustering algorithms. He et al. provide objective measures of clustering quality in their chapter. Applications of clustering methods to information retrieval is ad- dressed in the nex
出版日期Book 2004
關(guān)鍵詞algorithms; architecture; architectures; clustering; data mining; database; information; information retrie
版次1
doihttps://doi.org/10.1007/978-1-4613-0227-8
isbn_softcover978-1-4613-7949-2
isbn_ebook978-1-4613-0227-8Series ISSN 1568-1696
issn_series 1568-1696
copyrightKluwer Academic Publishers 2004
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Herkunft von Schwermetallen in B?dendisciplinary boundaries challenging if the organizing principles that construct the information architecture are not explicitly defined. Clustering data results across multiple information systems is challenging without a system architecture that provides both the data and distributed systems architecture and standards.
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Query Clustering in the Web Context,ing is a relatively new topic. The driving force of the development of query clustering techniques comes recently from the requirements of modern web searching Below we briefly analyze several motivations and applications of query clustering — FAQ detecting, index-term selection and query reformulation.
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https://doi.org/10.1007/978-3-658-09042-5ch for producing clusters of documents [4, 9, 16]. K-means clustering produces a set of un-nested clusters, and the top (most frequent or highest ”weight”) terms of the cluster are used to characterize the topic of the cluster. Once again, it is not unusual for some clusters to be mixtures of topics.
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