The Heterogeneous Cluster Ensemble Method Using Hubness for Clustering Text Documents

Hou, Jun; Nayak, Richi

doi:10.1007/978-3-642-41230-1_9

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Additionally, since it was demonstrated on several occasions that a better handling of hub points may result in better overall clustering quality in manydimensional problems [40,83,84], we intend to consider either extending the existing clustering quality indexes or proposing new ones that would incorporate this finding into account.…”

Section: Perspectives and Future Directionsmentioning

confidence: 97%

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Clustering Evaluation in High-Dimensional Data

Tomašev

Radovanović

2016

Unsupervised Learning Algorithms

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Clustering evaluation plays an important role in unsupervised learning systems, as it is often necessary to automatically quantify the quality of generated cluster configurations. This is especially useful for comparing the performance of different clustering algorithms as well as determining the optimal number of clusters in clustering algorithms that do not estimate it internally. Many clustering quality indexes have been proposed over the years and different indexes are used in different contexts. There is no unifying protocol for clustering evaluation, so it is often unclear which quality index to use in which case. In this chapter, we review the existing clustering quality measures and evaluate them in the challenging context of high-dimensional data clustering. High-dimensional data is sparse and distances tend to concentrate, possibly affecting the applicability of various clustering quality indexes. We analyze the stability and discriminative power of a set of standard clustering quality measures with increasing data dimensionality. Our evaluation shows that the curse of dimensionality affects different clustering quality indexes in different ways and that some are to be preferred when determining clustering quality in many dimensions.

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Section: Perspectives and Future Directionsmentioning

confidence: 97%

“…Hubness-based clustering has recently been proposed for high-dimensional clustering problems [83,84] and has been successfully applied in some domains like document clustering [40].…”

Section: Clustering Techniques For High-dimensional Datamentioning

confidence: 99%

Clustering Evaluation in High-Dimensional Data

Tomašev

Radovanović

2016

Unsupervised Learning Algorithms

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“…First, based on the data processing of the network coding structure (DPA), according to the different structure of the website, the data extraction method makes a corresponding solution, and realizes the different network coding based on the comprehensive utilization of the selenium automation framework, python analysis technology and data local access [8][9] [10]. A data processing algorithm for a structured data platform.…”

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confidence: 99%

Improved Data Analysis Algorithm based on Multi-feature Network Construction

Guo¹

2019

Proceedings of the 3rd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Network information has become an important factor in today's social environment and network environment. With the large coverage of network data traffic and the new generation of network technology, illegal network data is also constantly invading the network environment, which has caused a serious network security threat. Therefore, the analysis and research of network data and the pre-judgment of feature types are of great significance. Based on the existing theoretical techniques, this paper proposes a Data Processing Algorithm Based on The Website Coding Structure (DPA) and a Multi-feature Network Construction Improvement Algorithm (MCIA), processing network data and analysis type prediction features. Experiments show that the data processing algorithm based on the website coding structure has the advantage of targeted processing of website data. It also verifies the validity of the proposed Digital Neighborhood Feature Set Algorithm (DNFA) and the improved algorithm for multi-feature network construction for data analysis. Application, compared with the existing single feature set, the pre-judgment basis for constructing multiple feature sets is more reliable, and the pre-judgment basis is more credible.

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“…Existing clustering ensemble methods include co-association matrix based methods [47], Bayesian approaches [136], hyper-graph partitioning [119,53], mixture models [125,124] and evolutionary approach [145,87]. Apart from rare methods like [66] and [67], many of the above mentioned methods only select from candidate clustering solutions and are not able to construct new solutions out of the candidates. For example, by selecting some clusters from one candidate clustering solution and other clusters from another candidate clustering solution.…”

Section: Clustering Ensemblesmentioning

confidence: 99%

Improving Clustering Methods By Exploiting Richness Of Text Data

Wahid¹

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<p>Clustering is an unsupervised machine learning technique, which involves discovering different clusters (groups) of similar objects in unlabeled data and is generally considered to be a NP hard problem. Clustering methods are widely used in a verity of disciplines for analyzing different types of data, and a small improvement in clustering method can cause a ripple effect in advancing research of multiple fields. Clustering any type of data is challenging and there are many open research questions. The clustering problem is exacerbated in the case of text data because of the additional challenges such as issues in capturing semantics of a document, handling rich features of text data and dealing with the well known problem of the curse of dimensionality. In this thesis, we investigate the limitations of existing text clustering methods and address these limitations by providing five new text clustering methods--Query Sense Clustering (QSC), Dirichlet Weighted K-means (DWKM), Multi-View Multi-Objective Evolutionary Algorithm (MMOEA), Multi-objective Document Clustering (MDC) and Multi-Objective Multi-View Ensemble Clustering (MOMVEC). These five new clustering methods showed that the use of rich features in text clustering methods could outperform the existing state-of-the-art text clustering methods. The first new text clustering method QSC exploits user queries (one of the rich features in text data) to generate better quality clusters and cluster labels. The second text clustering method DWKM uses probability based weighting scheme to formulate a semantically weighted distance measure to improve the clustering results. The third text clustering method MMOEA is based on a multi-objective evolutionary algorithm. MMOEA exploits rich features to generate a diverse set of candidate clustering solutions, and forms a better clustering solution using a cluster-oriented approach. The fourth and the fifth text clustering method MDC and MOMVEC address the limitations of MMOEA. MDC and MOMVEC differ in terms of the implementation of their multi-objective evolutionary approaches. All five methods are compared with existing state-of-the-art methods. The results of the comparisons show that the newly developed text clustering methods out-perform existing methods by achieving up to 16\% improvement for some comparisons. In general, almost all newly developed clustering algorithms showed statistically significant improvements over other existing methods. The key ideas of the thesis highlight that exploiting user queries improves Search Result Clustering(SRC); utilizing rich features in weighting schemes and distance measures improves soft subspace clustering; utilizing multiple views and a multi-objective cluster oriented method improves clustering ensemble methods; and better evolutionary operators and objective functions improve multi-objective evolutionary clustering ensemble methods. The new text clustering methods introduced in this thesis can be widely applied in various domains that involve analysis of text data. The contributions of this thesis which include five new text clustering methods, will not only help researchers in the data mining field but also to help a wide range of researchers in other fields.</p>

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The Heterogeneous Cluster Ensemble Method Using Hubness for Clustering Text Documents

Cited by 9 publications

References 11 publications

Clustering Evaluation in High-Dimensional Data

Clustering Evaluation in High-Dimensional Data

Improved Data Analysis Algorithm based on Multi-feature Network Construction

Improving Clustering Methods By Exploiting Richness Of Text Data

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