K-modes Clustering

Chaturvedi, Anil; Green, Paul E.; Caroll, J. Douglas

doi:10.1007/s00357-001-0004-3

Cited by 229 publications

(106 citation statements)

References 12 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…To resolve this difficulty, Chaturvedi, Green, and Carroll (2001) proposed K-modes clustering, in which clusters are represented by category labels and the loss function is a counting measure.…”

Section: Clustering Models and Methodsmentioning

confidence: 99%

In memoriam, J. Douglas Carroll 1939–2011

Heiser

2012

Psychometrika

View full text Add to dashboard Cite

Section: Clustering Models and Methodsmentioning

confidence: 99%

In memoriam, J. Douglas Carroll 1939–2011

Heiser

2012

Psychometrika

View full text Add to dashboard Cite

“…The k-modes algorithm is similar to k-means, while the data in k-means should have numeric attributes, k-modes can cluster data with nominal (categorical) attributes [37].…”

Section: Partition-based Clustering Approachesmentioning

confidence: 99%

Distributed Gaussian Mixture Model Summarization Using the MapReduce Framework

Esmaeilpour

Bigdeli

Cheraghchi

et al. 2016

Advances in Artificial Intelligence

View full text Add to dashboard Cite

With an accelerating rate of data generation, sophisticated techniques are essential to meet scalability requirements. One of the promising avenues for handling large datasets is distributed storage and processing. Hadoop is a well-known framework for distributed storage and processing. Further, data summarization is a useful concept for managing large datasets. Data summarization techniques are intended to produce compact yet representative summaries for the entire dataset.Consolidation of these tools can allow a distributed implementation of data summarization. In this thesis, this goal is achieved by proposing and implementing a distributed Gaussian Mixture Model Summarization using the MapReduce framework (MR-SGMM). The main purpose of the proposed method is to summarize a dataset with a density-based clustering algorithm called DBSCAN algorithm, and then summarize each discovered cluster using the SGMM approach in a distributed manner. Testing the implementation with synthetic and real datasets is used to demonstrate its validity and efficiency.ii

show abstract

“…The clustering algorithm is the K-modes algorithm [7,10,12] which uses the K-means paradigm to cluster categorical data. Let W = [w n,k ] be an N-by-K matrix represents the partitioning of all records into K clusters, where one record is dedicated to one cluster, and let…”

Section: The Clustering Algorithmmentioning

confidence: 99%

“…They have shown that the K-modes algorithm converges in a finite number of iterations. For the K-modes algorithm, we refer to [7,12] for details. Here we first fix V and find necessary conditions on W to minimize F .…”

Section: The Clustering Algorithmmentioning

confidence: 99%

A semi-supervised regression model for mixed numerical and categorical variables

Chan

et al. 2007

Pattern Recognition

View full text Add to dashboard Cite

In this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-modes clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-modes parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least squares errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method.

show abstract

K-modes Clustering

Cited by 229 publications

References 12 publications

In memoriam, J. Douglas Carroll 1939–2011

In memoriam, J. Douglas Carroll 1939–2011

Distributed Gaussian Mixture Model Summarization Using the MapReduce Framework

A semi-supervised regression model for mixed numerical and categorical variables

Contact Info

Product

Resources

About