K-Centers Algorithm for Clustering Mixed Type Data

Zhao, Weidong; Dai, Weihui; Chun-bin, Tang

doi:10.1007/978-3-540-71701-0_129

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…Besides the aforementioned, k-centers [22] is an extension of the k-prototypes algorithm. It focuses on the effect of attribute values with different frequencies on clustering accuracy.…”

Section: Mixed-type Data Clustering Methodsmentioning

confidence: 99%

“…The weight parameters β and γ are for numerical and nominal attributes, respectively. According to [22], β is set to be 1 while a greater weight is given for γ if nominal valued attributes are emphasised more or a smaller value for γ otherwise. The new definition of centroids is also introduced.…”

Section: Mixed-type Data Clustering Methodsmentioning

confidence: 99%

“…Other attempts focus on defining a distance metric that is effective for the evaluation of dissimilarity amongst data objects in a mixed-type dimensional space. These include different extensions of k-means, k-prototypes [21] and k-centers [22], respectively. Similar to most clustering methods, the aforementioned models are parameterized, thus achieving optimal performance may not be possible across diverse data collections.…”

Section: Introductionmentioning

confidence: 99%

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Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

Boongoen¹,

Iam-On²

2022

Computers, Materials &Amp; Continua

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A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to characterize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, LCE WCT and LCE WTQ that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different initializations of the k-prototypes algorithm as base clusterings and then refine the summarized data using a link-based approach. Based on the evaluation metric of NMI (Normalized Mutual Information) that is averaged across different combinations of benchmark datasets and experimental settings, these new models reach the improved level of 0.34, while the best model found in the literature obtains only around the mark of 0.24. Besides, parameter analysis included herein helps to enhance their performance even further, given relations of clustering quality and algorithmic variables specific to the underlying link-based models. Moreover, another significant factor of ensemble size is examined in such a way to justify a tradeoff between complexity and accuracy.

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“…Besides the aforementioned, k-centers [22] is an extension of the k-prototypes algorithm. It focuses on the effect of attribute values with different frequencies on clustering accuracy.…”

Section: Mixed-type Data Clustering Methodsmentioning

confidence: 99%

Section: Mixed-type Data Clustering Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

Boongoen¹,

Iam-On²

2022

Computers, Materials &Amp; Continua

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“…Their results suggest an improvement in clustering results with feature weights over the clustering results achieved with the K-prototypes algorithm [9], [10]. Zhao et al [24] use the frequency of feature values for categorical features to define the cluster centers. The Hamming distance measure was used to compute the distance for categorical features, whereas mean values are used for numeric features.…”

Section: A Partitional Clusteringmentioning

confidence: 99%

Survey of State-of-the-Art Mixed Data Clustering Algorithms

2019

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Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field. INDEX TERMS Categorical features, clustering, mixed datasets, numeric features. I. INTRODUCTION Clustering is an unsupervised machine learning technique used to group unlabeled data into clusters that contain data points that are 'similar' to each other and 'dissimilar' from those in other clusters [1], [2]. Many clustering algorithms can only handle data that contain either numeric or categorical feature values [3], [4]. Numeric features can take real values, such as height, weight, and distance. Categorical features represent data that can be divided into a fixed number of categories, such as color, race, sex, profession, and blood group. Clustering algorithms group data points into clusters using some notion of 'similarity', which can be as simple as the Euclidean distance. To compute the similarity between numeric feature values, mathematical operations (such as distances, angles, summation, or mean) are applied to them. Distance-based similarity measures are mostly used for numeric data points. Generally, categorical feature values are not inherently ordered (for example, the categorical values, red and blue). It is not possible to directly compute the distance between two categorical feature values. Therefore, computing distance-based similarity measures for categorical data is a challenging task [5]. Nevertheless, several methods The associate editor coordinating the review of this manuscript and approving it for publication was Haruna Chiroma.

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“…As the extension of K Prototype Clustering algorithm, The K center Algorithm has been proposed and proved that the algorithm contributes improved results by considering the frequency of attributes in consideration [8].…”

Section: Background Knowledgementioning

confidence: 99%

Map Reduce based REmoving Dependency on K and Initial Centroid Selection MR-REDIC Algorithm for clustering of Mixed Data

Nirmal¹,

Satyanarayana²

2020

IJACSA

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In machine learning, clustering is recognized as widely used task to find hidden structure of data. While handling the massive amount of data, the traditional clustering algorithm degrades in performance due to size and mixed type of attributes. The Removal Dependency on K and Initial Centroid Selection (REDIC) algorithm is designed to handle mixed data with frequency based dissimilarity measurement for categorical attributes. The selection of initial centroids and prior decision for number of cluster improves the efficiency of REDIC algorithm. To deal with the large scale data, the REDIC algorithm is migrated to Map Reduce paradigm,and Map Reduce based REDIC(MR-REDIC) algorithm is proposed. The large amount of data is divided into small chunks and parallel approach is used to reduce the execution time of algorithm.The proposed algorithm inherits the feature of REDIC algorithm to cluster the data.The algorithm is implemented in Hadoop environment with three different configuration and evaluated using five bench mark data sets. Experimental results show that the Speed up value of data is gradually shifting towards linear by increasing number of data nodes from one to four. The algorithm also achieves the near to closer value for Scale up parameter, while maintaining the accuracy of algorithm.

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K-Centers Algorithm for Clustering Mixed Type Data

Cited by 14 publications

References 3 publications

Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

Survey of State-of-the-Art Mixed Data Clustering Algorithms

Map Reduce based REmoving Dependency on K and Initial Centroid Selection MR-REDIC Algorithm for clustering of Mixed Data

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