Co-clustering for Binary and Categorical Data with Maximum Modularity

Labiod, Lazhar; Nadif, Mohamed

doi:10.1109/icdm.2011.37

Cited by 33 publications

(28 citation statements)

References 12 publications

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“…Additionaly, due to the unavailability of SpecCo source code, the results presented here will be compared with those reported in [4], with proper observations.…”

Section: Comparative Reviewmentioning

confidence: 99%

“…They also found that their algorithm was scalable regarding data set size. Another notable algorithm is the so called SpecCo [4] which reformulates the Co-Clustering problem as a graph partitionoing problem. It then applies a modularity maximization algorithm in order to find a k, l-coclusters set.…”

Section: Comparative Reviewmentioning

confidence: 99%

“…Co-Clustering refers to the process of finding subsets of rows and columns from a data matrix such as the values of the extracted submatrix presents any kind of relationship [2], [3], [4], [5], [6], [7], [8], [9]. Usually, the rows and columns are described as objects, or samples, and features respectivelly from a Data Mining perspective.…”

Section: Co-clusteringmentioning

confidence: 99%

“…Data Co-Clustering [2], [3], [4], [5], also known as biclustering, tries to find subsets of samples and features that maximizes the similarity of the select samples when considering the chosen features. It exploits the fact that a given sample may belong to different categories when viewed by different aspects of its description.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the creation of algorithms with such flexibilities is not trivial, and such algorithms have implications that make it difficult to make a basis of comparison with the literature and give theoretical guarantees. As such, many Co-Clustering algorithms were created restraining some of these flexibilities by impeding the overlapping of samples and features among groups and pre-specifying the number of samples and features clusters [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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A hash-based co-clustering algorithm for categorical data

França

2016

Expert Systems with Applications

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Abstract-Many real-life data are described by categorical attributes without a pre-classification. A common data mining method used to extract information from this type of data is clustering. This method group together the samples from the data that are more similar than all other samples. But, categorical data pose a challenge when extracting information because: the calculation of two objects' similarity is usually done by measuring the number of common features, but ignore a possible importance weighting; if the data may be divided differently according to different subsets of the features, the algorithm may find clusters with different meanings from each other, difficulting the post analysis. Data Co-Clustering of categorical data is the technique that tries to find subsets of samples that share a subset of features in common. By doing so, not only a sample may belong to more than one cluster but, the feature selection of each cluster describe its own characteristics. In this paper a novel Co-Clustering technique for categorical data is proposed by using Locality Sensitive Hashing technique in order to preprocess a list of Co-Clusters seeds based on a previous research. Results indicate this technique is capable of finding high quality Co-Clusters in many different categorical data sets and scales linearly with the data set size.

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“…Additionaly, due to the unavailability of SpecCo source code, the results presented here will be compared with those reported in [4], with proper observations.…”

Section: Comparative Reviewmentioning

confidence: 99%

Section: Comparative Reviewmentioning

confidence: 99%