Multi-view clustering of web documents using multi-objective genetic algorithm

Wahid, Abdul; Gao, Xiaoying; Andreae, Peter

doi:10.1109/cec.2014.6900586

Cited by 19 publications

(5 citation statements)

References 33 publications

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“…Here, we further discuss the existing multi-objective approaches to MVC (Section II-C) from the perspective of two important design aspects: the solution representation used and the ability to automatically determine the number of clusters, k. With a few exceptions [21], [27], [28], most metaheuristic methods adopt either a label-based or a prototype-based representation. On the one hand, the label-based approach directly encodes cluster memberships [16], [24], specifying which cluster each of the N data entities belongs to.…”

Section: Discussion Of the Related Workmentioning

confidence: 99%

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Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Garza-Fabre

Handl

José-García

2023

IEEE Trans. Evol. Computat.

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Multi-view data analysis provides an effective means to integrate the distinct information sources which are inherent to many applications. Data clustering in a multi-view setting specifically aims to identify the most appropriate grouping for a collection of entities, where those entities (or their relationships) can be described from multiple perspectives. Leveraging recent advances in multi-objective clustering, we propose a new evolutionary method to tackle this challenge. Designed around a flexible and unbiased solution representation, together with strategies based on the minimum spanning tree and neighborhood relations, our algorithm optimizes multiple objectives simultaneously to effectively explore the space of candidate trade-offs between the data views. Through a series of experiments, we investigate the suitability of our proposal in the context of a bioinformatics application, clustering of plausible protein structures, and a diverse set of synthetic problems. The specific case of two data views is considered in this paper. The evaluation with respect to a variety of reference approaches demonstrates the effectiveness of our method in discovering high-quality partitions in a multiview setting. Robustness against unreliable data sources and the ability to automatically determine the number of clusters, are additional advantages evidenced by the results obtained.

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Section: Discussion Of the Related Workmentioning

confidence: 99%

“…Perhaps the first application of MOEAs in the context of MVC is the one reported by Wahid et al [27], [28]. In fact, multiple views are used only during the initial stage to seed the proposed clustering ensemble method with a diverse set of partitions.…”

Section: Multi-objective Approach To Multi-view Clusteringmentioning

confidence: 99%

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Garza-Fabre

Handl

José-García

2023

IEEE Trans. Evol. Computat.

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“…Ensemble clustering combines the multiple results of different clustering algorithms to obtain final results. Multiview clustering is an extension of ensemble clustering and combines different data that have different properties and views [34,35].…”

Section: Clustering Algorithmmentioning

confidence: 99%

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Zhang

Zhao

et al. 2017

Mathematical Problems in Engineering

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Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

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“…The second method MOCK is a multi-objective evolutionary algorithm [62] based on SPEA-II 8 . Our recent work, MMOEA is a multi-objective multi-view evolutionary algorithm based on the NSGA-II approach [132]. MMOEA uses the standard crossover method with three objectives.…”

Section: Comparison On Two View Datasetsmentioning

confidence: 99%

“…and the fitness evaluation criteria which are both domain specific. In previous chapter we discussed a Multi-view Multi-Objective Evolutionary Algorithm (MMOEA) [132] which uses multiple views to generate an initial set of candidate clustering solutions and then applies an evolutionary approach (NSGA-II) using a simple crossover method and three objective functions to select better quality clusters. MMOEA has several limitations.…”

Section: Introductionmentioning

confidence: 99%

Improving Clustering Methods By Exploiting Richness Of Text Data

Wahid¹

Self Cite

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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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Multi-view clustering of web documents using multi-objective genetic algorithm

Cited by 19 publications

References 33 publications

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Improving Clustering Methods By Exploiting Richness Of Text Data

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