Applying Biclustering to Text Mining: An Immune-Inspired Approach

Castro, Pablo A. D.; França, Fabrício Olivetti de; Ferreira, Hamilton M.; Zuben, Fernando J. Von

doi:10.1007/978-3-540-73922-7_8

Cited by 61 publications

(33 citation statements)

References 10 publications

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“…1. Now besides in biological data analysis, the biclustering technique has been applied successfully in other interesting fields, including information retrieval, text mining (de Castro et al, 2007), market data analysis (Dolnicar et al, 2012), recommendation systems (Inbarani and Thangavel, 2011), financial forecasting and trading (Huang, 2011), and collaborative filtering (Symeonidis et al, 2008). It has become a powerful tool of data mining, particularly in analyzing high-dimensional datasets.…”

Section: The Basic Idea Of Biclustering Techniquementioning

confidence: 99%

A biclustering-based method for market segmentation using customer pain points

Wang

Miao

Zhao

et al. 2016

Engineering Applications of Artificial Intelligence

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Section: The Basic Idea Of Biclustering Techniquementioning

confidence: 99%

A biclustering-based method for market segmentation using customer pain points

Wang

Miao

Zhao

et al. 2016

Engineering Applications of Artificial Intelligence

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“…The main motivation is to find data points that are correlated under only a subset of the attributes, which is above the capabilities of usual clustering methods. Some examples of biclustering applications are dimensionality reduction [3], information retrieval and text mining ( [4], [5]), electoral data analysis [6], collaborative filtering ( [7]) and biological data analysis [3]. In this paper, the biclustering approach will be employed to find coherence inside biological data on microarray experiments.…”

Section: Biclusteringmentioning

confidence: 99%

Finding a high coverage set of 5-biclusters with swarm intelligence

França

Zuben

2010

IEEE Congress on Evolutionary Computation

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Biclustering is usually referred to as the process of finding subsets of rows and columns from a given dataset. Each subset is a bicluster and corresponds to a sub-matrix whose elements tend to present a high degree of coherence with each other. In order to find such structures, the δ-biclustering problem was formulated, being denoted as the problem of finding a set of biclusters limited by a maximum degree of coherence, measured by a mean-squared residue, while maximizing the bicluster total size. Additionally, it is expected a reduced overlap among the biclusters in the set, in other words, a minimization of the number of common elements shared by them. This also leads to a high coverage of the original dataset given the number of biclusters found. Most algorithms intended to find such biclusters focus only on the mean-squared residue and/or the bicluster size. This usually leads to a set of biclusters that do not fully cover the whole data and, as a consequence, shares a high overlap among them. This may generate redundant information on some portions of the dataset and lack of information on other portions. Also, some methods introduce noise into the dataset in order to promote a better coverage, but sometimes misleading the search. In this paper, a swarm-based approach, named SwarmBcluster, is created to effectively find biclusters without introducing noise and with the main objective of achieving maximum coverage. Experiments were performed considering two well-known datasets and a comparative analysis considering other approaches indicates that SwarmBcluster is capable of finding a set of biclusters with high coverage, while maintaining a high average volume and also obeying the coherence constraint imposed.

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“…dopt-aiNet (Artificial Immune Network for Dynamic Optimization [62]) is an improved and extended version of opt-aiNet for timevarying fitness functions. Recently, bic-aiNet (Artificial Immune Network for Biclustering) was proposed by de Castro et al [63] to generate biclusters. In all these works, the authors have explored the same stream of immune inspiration and obtained competitive results when compared to the literature.…”

Section: The Omni-ainet Algorithmmentioning

confidence: 99%

An immune-inspired multi-objective approach to the reconstruction of phylogenetic trees

Coelho

Silva

Zuben

2010

Neural Comput & Applic

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This work presents the application of the omniaiNet algorithm-an immune-inspired algorithm originally developed to solve single and multi-objective optimization problems-to the reconstruction of phylogenetic trees. The main goal here is to automatically evolve a population of phylogenetic unrooted trees, possibly with distinct topologies, by minimizing at the same time two optimization criteria: the minimum evolution and the mean-squared error. This proposal generates, in a single run, a set of nondominated solutions that represent the trade-offs of the two conflicting objectives, and gives the user the possibility of having distinct explanations for the differences observed at the terminal nodes of the trees. A series of experimental results is also reported in this work, in order to illustrate the effectiveness of the proposal and its capability to overcome the restrictive feedback provided by the application of well-known algorithms for phylogenetic reconstruction, such as the Neighbor Joining. Besides, the methodology presented in this work is compared to the popular NSGA-II algorithm, also modified to solve phylogenetic reconstruction problems.

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Applying Biclustering to Text Mining: An Immune-Inspired Approach

Cited by 61 publications

References 10 publications

A biclustering-based method for market segmentation using customer pain points

A biclustering-based method for market segmentation using customer pain points

Finding a high coverage set of 5-biclusters with swarm intelligence

An immune-inspired multi-objective approach to the reconstruction of phylogenetic trees

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