Survey of Text Mining I: Clustering, Classification, and Retrieval
Michael W. Berry
Language: English
Pages: 254
ISBN: 2:00052614
Format: PDF / Kindle (mobi) / ePub
Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory.
As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments.
This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.
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(p) ) − 2 spφ = trace(K ¯ (p) e K n(p) 1 . (3.6) 56 D. Zeimpekis, E. Gallopoulos We call the proposed algorithm KPDDP(l) (Kernel PDDP(l)). The algorithm is tabulated in Algorithm 3.4.1. As in the basic algorithm, at each step the cluster node p with the largest scatter value [Eq. (3.6)] is selected and partitioned into 2l subclusters. Each member of the selected node is classified into the subcluster defined by the combination of its projection coefficients [Eq. (3.5)] into the l.
Of document space. In some three-dimensional projected views, clusters overlap to a greater or lesser extent, or more than one pair may overlap. 6.6 Directions for Future Work This chapter concludes by mentioning a number of interesting questions and open problems in information retrieval and clustering: 6 Vector Space Models for Search and Cluster Mining 123 100 80 60 40 20 0 LSI COV 0% 65% LSI-IR 60% COV-SR1 100% COV-SR2 100% Fig. 6.4. Clusters (major and minor combined) retrieved.
Semidefinite programming solver — merely specifying the constraints (taking full advantage of their sparsity) requires prohibitive amount of memory.” Numerical experiments with the SeDuMi package [Stu99] against problems of n ≥ 50 confirm this conclusion. With a semidefinite matrix K, we can apply the kernel method and MDS on XML document representation. 7.5 Newton-Type Method In this section, we will show how a Newton-type method in [QS06] can be applied to the problem (7.3). Actually, problem.
Satisfy. The optimality condition of the dual problem (7.5) is A (G + A∗ z)+ = b, z∈ n . (7.6) Once a solution z ∗ of (7.6) is found, we can construct the optimal solution of (7.4) by 138 Z. Xia, G. Xing, H. Qi, Q. Li K ∗ = (G + A∗ z ∗ )+ . (7.7) A very important question on K ∗ is when it is of full rank (i.e., rank(K ∗ ) = n). Equivalently, one may ask when K ∗ is rank-deficient (i.e., rank(K ∗ ) < n). An interesting interpretation of the rank of K ∗ is that the number rank(K ∗ ).
ProductCd-0471986917.html. [SBPP06] F. Shahnaz, M.W. Berry, V.P. Pauca, and R.J. Plemmons. Document clustering using non-negative matrix factorization. Information Processing & Management, 42(2):373–386, 2006. [SGB00] N.D. Sidiropoulos, G.B. Giannakis, and R. Bro. Blind PARAFAC receivers for DS-CDMA systems. IEEE Transactions on Signal Processing, 48(3):810–823, 2000. [SZL+ 05] J.-T. Sun, H.-J. Zeng, H. Liu, Y. Lu, and Z. Chen. CubeSVD: a novel approach to personalized Web search. In WWW 2005:.