A Mixed Data Clustering Algorithm to Identify Population Patterns of Cancer Mortality in Hijuelas-Chile

Malo, Eileen; Salas, Rodrigo; Catalán, Mónica; López, Patricia

doi:10.1007/978-3-540-73599-1_25

“…The study leads to a better understanding of metabolic syndrome (MetS). Malo et al [175] use mixed data clustering to study people who died of cancer between 1994 and 2006 in Hijuelas. Storlie et al [176] develop modelbased clustering for mixed datasets with missing feature values to cluster autism spectrum disorder.…”

Section: ) Health and Biologymentioning

confidence: 99%

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

Ahmad

¹

,

Khan

²

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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“…The study leads to a better understanding of metabolic syndrome (MetS). Malo et al [175] use mixed data clustering to study people who died of cancer between 1994 and 2006 in Hijuelas. Storlie wt al.…”

Section: A: Health and Biologymentioning

confidence: 99%

Survey of state-of-the-art mixed data clustering algorithms

Ahmad,

Khan

2018

Preprint

0

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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 is 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 a state-of-theart review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. Lastly, 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.

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“…The goal of clustering is to form groups of objects with similar characteristics. More recently, clustering has been used for scientific discovery, for instance in medical field to identify cancers clusters [1], [2], and in environmental sciences where scientists look for associations between pollutants and other factors [3].…”

mentioning

confidence: 99%

An architecture and algorithms for multi-run clustering

Jiamthapthaksin

¹

,

Eick

²

,

Rinsurongkawong

³

2009

2009 IEEE Symposium on Computational Intelligence and Data Mining

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Abstract-This paper addresses two main challenges for clustering which require extensive human effort: selecting appropriate parameters for an arbitrary clustering algorithm and identifying alternative clusters. We propose an architecture and a concrete system MR-CLEVER for multi-run clustering that integrates active learning with clustering algorithms. The key hypothesis of this work is that better clustering results can be obtained by combining clusters that originate from multiple runs of clustering algorithms. By defining states that represent parameter settings of a clustering algorithm, the proposed architecture actively learns a state utility function. The utility of a parameter setting is assessed based on clustering run-time, quality and novelty of the obtained clusters. Furthermore, the utility function plays an important role in guiding the clustering algorithm to seek novel solutions. Cluster novelty measures are introduced for this purpose. Finally, we also contribute a cluster summarization algorithm that assembles a final clustering as a combination of high-quality clusters originating from multiple runs. Merits of our proposed system are that it is generic and therefore can be used in conjunction with different clustering algorithms, and it reduces human effort for selecting the parameters, for comparing clustering results and for assembling clustering results. We evaluate the proposed system in conjunction with a representative based clustering algorithm namely CLEVER for a challenging data mining task involving an earthquake dataset. The obtained results demonstrate that, in comparison to the best single-run clustering, multi-run clustering discovers solutions of higher quality.

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A Mixed Data Clustering Algorithm to Identify Population Patterns of Cancer Mortality in Hijuelas-Chile

Cited by 3 publications

References 2 publications

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

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

Survey of state-of-the-art mixed data clustering algorithms

An architecture and algorithms for multi-run clustering

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