A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data

Ji, Jinchao; Pang, Wei; Zhou, Chunguang; Han, Xiao; Wang, Zhe

doi:10.1016/j.knosys.2012.01.006

Cited by 141 publications

(60 citation statements)

References 25 publications

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“…Moreover, we also carried out experiments using the traditional K-Means algorithm on the data sets containing solely numerical features. We did not include in our simulations other popular 255 clustering algorithms capable of dealing with data sets containing categorical data, such as k-prototype, SBAC and KL-FCM-GM [19, 10,17], because WK-DC was already shown to outperform all of them [18]. Table 2 shows the results of our experiments in terms of the adjusted Rand index (ARI) and the number of completed iterations necessary for convergence (Itr).…”

Section: Resultsmentioning

confidence: 99%

“…Such a diversification of clustering algorithms can be explained by a variety of different ways in which data groups, nor-10 mally referred to as clusters, may be formed. Some algorithms allow a given entity to belong to two or more clusters, sometimes even with different degrees of membership, but most of them allow it to belong to a single cluster only [9,10]. In this paper, we are particularly interested in the latter approach, including data partitioning algorithms with a crisp membership.…”

Section: Introductionmentioning

confidence: 99%

“…Huang [17] introduced the k-prototype algorithm, a popular variation of K-Means designed to deal directly with data sets containing mixed data types. This approach has been also extended to fuzzy clustering [10]. 50 Kim et al [9] introduced the concept of fuzzy centroids.…”

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confidence: 99%

“…tioning algorithms such as k-prototype, SBAC and KL-FCM-GM [19,10]. The WK-DC algorithm deals with data sets containing both numerical and categorical features, but still represents each partition using a single centroid C = {c 1 , c 2 , ..., c K }.…”

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confidence: 99%

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Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Amorim

Makarenkov

2016

Neurocomputing

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We consider the weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different weights at different clusters. Thus, it supports the intuitive idea that features may have different degrees of relevance at different clusters. We use the Minkowski metric in a way that feature weights become feature re-scaling factors for any considered exponent. Moreover, the traditional Silhouette clustering validity index was adapted to deal with both numerical and categorical types of features. Finally, we show that our new method usually outperforms traditional K-Means as well as the recently proposed WK-DC clustering algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Amorim

Makarenkov

2016

Neurocomputing

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“…Nonetheless, using hard clustering for GDA often leads to the issues of ecological fallacy, which can be shortly understood that statistics accurately describing group characteristics do not necessarily apply to individuals within that group. For this fact, Fuzzy C-Means (FCM) and its variants were considered as the appropriate methods to determine the distribution of a demographic feature on a map as described in some articles such as [1,5,10,[12][13][14][15][16][17][18][19]. Since the results of FCM are independent to the geographical factors, some improvements of that algorithm were made by attaching FCM with a spatial model such as SIM in [3] and SIM-PF in [7,16,18].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approaches to Context Variables in Fuzzy Geographically Weighted Clustering

Minh¹,

Sơn²

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Fuzzy Geographically Weighted Clustering (FGWC) is considered as a suitable tool for the analysis of geo-demographic data that assists the provision and planning of products and services to local people. Context variables were attached to FGWC in order to accelerate the computing speed of the algorithm and to focus the results on the domain of interests. Nonetheless, the determination of exact, crisp values of the context variable is a hard task. In this paper, we propose two novel methods using fuzzy approaches for that determination. A numerical example is given to illustrate the uses of the proposed methods.

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Unsupervised Learning

Giovanni¹

2018

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article presents a review of traditional and current methods of classification in the framework of unsupervised learning. Focus is placed on cluster analysis and self‐organizing neural networks: two vector quantization methods aiming at minimizing the distance between an input vector and its representation. The learning is unsupervised as no predefined cluster structure of the input data is assumed. The review of cluster analysis methods covers (i) hard clustering, hierarchical and nonhierarchical, whose aim is to assign exact units (objects) to clusters (i.e., with membership degree equal to 1); (ii) fuzzy clustering, where the membership degree of a unit to a cluster is in the range [0; 1]; and (iii) mixture clustering, a model‐based clustering consisting in fitting a mixture model to data and identifying each cluster with one of its components. These clustering methods are reviewed in all the variants related to the presence of complex and/or big data structures and to the presence of outliers. The self‐organizing maps are also presented as artificial neural network, the cells (neurons) of which become specifically tuned to various input data patterns or classes of patterns through an unsupervised learning process.

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A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data

Cited by 141 publications

References 25 publications

Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Fuzzy Approaches to Context Variables in Fuzzy Geographically Weighted Clustering

Unsupervised Learning

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