Text mining without document context

SanJuan, Eric; Ibekwe-Sanjuan, Fidelia

doi:10.1016/j.ipm.2006.03.017

Cited by 54 publications

(12 citation statements)

References 27 publications

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“…The CPCL algorithm has been evaluated against variants of hierarchical clustering and k-means and was found to produce more homogeneous clusters. See [3] for more details.…”

Section: Term Clusteringmentioning

confidence: 99%

Decomposition of terminology graphs for domain knowledge acquisition

Ibekwe-Sanjuan

SanJuan

Vogeley

2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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We propose a graph decomposition algorithm for analyzing the structure of complex graph networks. After multiword term extraction, we apply techniques from text mining and visual analytics in a novel way by integrating symbolic and numeric information to build clusters of domain topics. Terms are clustered based on surface linguistic variations and clusters are inserted in an association network based on their intersection with documents. The graph is then decomposed based on atom graph structure into central (non-decomposable) atom and peripheral atoms. The whole process is applied to publications from the Sloan Digital Sky Survey (SDSS) project in the Astronomy field. The mapping obtained was evaluated by a domain expert and appeared to have captured interesting conceptual relations between different domain topics.

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“…The CPCL algorithm has been evaluated against variants of hierarchical clustering and k-means and was found to produce more homogeneous clusters. See [3] for more details.…”

Section: Term Clusteringmentioning

confidence: 99%

Decomposition of terminology graphs for domain knowledge acquisition

Ibekwe-Sanjuan

SanJuan

Vogeley

2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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“…It has been shown that this variant of hierarchical clustering preserves its main ultrametric properties [15]. The clustering algorithm is implemented using a straightforward O(E) procedure called Select Local Maximum Edge (SLME) [16].…”

Section: Classification By Preferential Clustered Link (Cpcl)mentioning

confidence: 99%

Visual Analysis of Conflicting Opinions

Chen

Ibekwe-Sanjuan

SanJuan

et al. 2006

2006 IEEE Symposium on Visual Analytics and Technology

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Understanding the nature and dynamics of conflicting opinions is a profound and challenging issue. In this paper we address several aspects of the issue through a study of more than 3,000 Amazon customer reviews of the controversial bestseller The Da Vinci Code, including 1,738 positive and 918 negative reviews. The study is motivated by critical questions such as: What are the differences between positive and negative reviews? What is the origin of a particular opinion? How do these opinions change over time? To what extent can differentiating features be identified from unstructured text? How accurately can these features predict the category of a review? We first analyze terminology variations in these reviews in terms of syntactic, semantic, and statistic associations identified by TermWatch and use term variation patterns to depict underlying topics. We then select the most predictive terms based on log likelihood tests and demonstrate that this small set of terms classifies over 70% of the conflicting reviews correctly. This feature selection process reduces the dimensionality of the feature space from more than 20,000 dimensions to a couple of hundreds. We utilize automatically generated decision trees to facilitate the understanding of conflicting opinions in terms of these highly predictive terms. This study also uses a number of visualization and modeling tools to identify not only what positive and negative reviews have in common, but also they differ and evolve over time.

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“…Clustering usually aims at finding compact and clearly separated clusters. Clustering techniques have been applied in domains such as sensory time series (Yin and Yang, 2005) and text mining (SanJuan and Ibekwe-SanJuan, 2006). This paper presents an iterative methodology for supporting system identification using clustering.…”

Section: Introductionmentioning

confidence: 99%

Improving System Identification Using Clustering

Saitta

Kripakaran

Raphael

et al. 2008

J. Comput. Civ. Eng.

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System identification involves identification of a behavioral model that best explains the measured behavior of a structure. This research uses a strategy of generation and iterative filtering of multiple candidate models for system identification. The task of model filtering is supported by measurement cycles. During each measurement cycle, the location for subsequent measurement can be chosen using the predictions of current candidate models. In this paper, data mining techniques are proposed to support such measurement-interpretation cycles. Candidate models, representing possible states of a structure, are clustered using a technique that combines principal component analysis and K-means clustering. Representative models of each cluster are used to place sensors for subsequent measurement on the basis of the entropy of their predictions. Models are filtered from candidate model sets using new measurements. Results show that clustering is necessary to identify the different groups of candidate models. The entropy of predictions is found to be a valid stopping criterion for iterative sensor addition. While measurement-interpretation cycles can lead to a unique model for structures with low levels of complexity, engineers may be left with large numbers of models for structures with higher levels of uncertainty. In those situations, clustering is a powerful tool to classify models and thus provide much fewer representative models to engineers for further decision making.

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Text mining without document context

Cited by 54 publications

References 27 publications

Decomposition of terminology graphs for domain knowledge acquisition

Decomposition of terminology graphs for domain knowledge acquisition

Visual Analysis of Conflicting Opinions

Improving System Identification Using Clustering

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