Model-based multidimensional clustering of categorical data

Chen, Tao; Zhang, Nevin L.; Liu, Tengfei; Poon, Leonard K. M.; Wang, Yi

doi:10.1016/j.artint.2011.09.003

Cited by 102 publications

(74 citation statements)

References 37 publications

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“…Unidimensional clustering is unable to uncover such partitions because it seeks one partition that is jointly defined by all the attributes. They proposed a method for multidimensional clustering, namely latent tree analysis [16]. Cao et al presented a weighting k-Modes algorithm for subspace clustering of categorical data and its corresponding time complexity is analysed as well.…”

Section: B Recently Used Algorithmsmentioning

confidence: 99%

Different techniques designs for clustering of data, specially categorical data: An Overview

Bhatt¹

2014

IOSRJEN

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-Mining of knowledge from a pool of data is known as data mining. One of the research potent field of research in area of data mining is clustering. The earlier work in clustering is mainly concentrated on quantitative and ordinal data or we can in general say numeric data which have an inherited order. But application of these algorithms on categorical data is not possible as these data items do not have any inherited order. Therefore it becomes necessary to have algorithms that can solve the problem of categorical data clustering. In this paper, an overview of various categorical data clustering techniques has been given.

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Section: B Recently Used Algorithmsmentioning

confidence: 99%

Different techniques designs for clustering of data, specially categorical data: An Overview

Bhatt¹

2014

IOSRJEN

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“…However, he does not describe how this can done in detail at that time. This idea is further developed through the subsequent work of Chen [24] and Chen et al [28].…”

Section: Multidimensional Clusteringmentioning

confidence: 99%

“…EAST is considered as the state-of-the-art method for learning LTMs. It is described in more details by Chen et al [28]. Recently, methods using other approaches have been proposed for learning LTMs.…”

Section: Eastmentioning

confidence: 99%

“…They allow us to cluster the students along three different dimensions. Hence, this approach to clustering is known as multidimensional clustering [28]. Note that while both hierarchical clustering and multidimensional clustering yield multiple clusterings, there is an important difference between them.…”

Section: Multidimensional Clusteringmentioning

confidence: 99%

“…The ratio measures the unit improvement of m over m. It is also related to the likelihood ratio test in a later work [28]. Among those candidate models with more parameters than the base model, the cost-effective principle stipulates that the one with the highest improvement ratio should be chosen.…”

Section: Operation Granularitymentioning

confidence: 99%

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Classical Datamining methods are facing various challenges in the era of Big Data. Between the need of fast knowledge extraction and the high flows of data acquired in small slots of time, these methods became shifted. The variability and the veracity of the Big Data perplex the Machine Learning process. The high volume of Big Data yields to a congested learning because the classic methods are designed for small sets of features. Deep Learning has recently emerged in the aim of handling voluminous data. The concept of the Deep induces the conversion of the features into a new abstracted representation in order to optimize an objective. Although the Deep Learning methods are experimentally promising, their parameterization is exhaustive and empirical.To tackle these problems, we utilize the causality and the uncertainty of the Bayesian Network in order to propose a new Deep Bayesian Network architecture. We provide a new learning algorithm for this multi-layered Bayesian Network with latent variables. We evaluate the proposed architecture and learning algorithms over benchmark datasets. We used high-dimensional data in order to simulate the Big Data challenges, which are imposed by the volume and veracity aspects. We demonstrate the effectiveness of our contribution under these constraints.

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Model-based multidimensional clustering of categorical data

Cited by 102 publications

References 37 publications

Different techniques designs for clustering of data, specially categorical data: An Overview

Different techniques designs for clustering of data, specially categorical data: An Overview

Latent tree models : an application and an extension

Deep Bayesian network architecture for Big Data mining

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