Distance‐based clustering of mixed data

Velden, Michel van de; D’Enza, Alfonso Iodice; Markos, Angelos

doi:10.1002/wics.1456

Cited by 64 publications

(40 citation statements)

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“…Our dataset consisted of both categorical and continuous variables. Despite the problem of combining both categorical and continuous data, usually referred to as "mixed data", in learning algorithms is well known in literature (16) and usually requires sophisticated and dedicated strategies (17). However, it is known that some algorithms, like the ones used in this work, can manage these data (for several purposes) using simple numeric encoding (18)(19)(20)(21).…”

Section: Methodsmentioning

confidence: 99%

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results

et al. 2021

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The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.

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Section: Methodsmentioning

confidence: 99%

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results

et al. 2021

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“…As (k + T + 1) << n , we can see this is much less computationally intensive than, for instance, calculating Gower’s similarity coefficient and applying multidimensional scaling. See references [ 39 , 40 ] for comprehensive reviews of clustering methods for mixed data.

where

for

and

for

.…”

Section: Methodsmentioning

confidence: 99%

Visualizing Inequality in Health and Socioeconomic Wellbeing in the EU: Findings from the SHARE Survey

Grané

Albarrán

Lumley

2020

IJERPH

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The main objective of this paper is to visualize profiles of older Europeans to better understand differing levels of dependency across Europe. Data comes from wave 6 of the Survey of Health, Ageing and Retirement in Europe (SHARE), carried out in 18 countries and representing over 124 million aged individuals in Europe. Using the information of around 30 mixed-type variables, we design four composite indices of wellbeing for each respondent: self-perception of health, physical health and nutrition, mental agility, and level of dependency. Next, by implementing the k-prototypes clustering algorithm, profiles are created by combining those indices with a collection of socio-economic and demographic variables about the respondents. Five profiles are established that segment the dataset into the least to the most individuals at risk of health and socio-economic wellbeing. The methodology we propose is wide enough to be extended to other surveys or disciplines.

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“…However, they are not detailed and they concentrate on specific types of clustering algorithms. Velden et al [11] study five distance-based clustering algorithms for mixed data on three mixed datasets. They conclude that there is no single clustering approach that performs well for all the datasets.…”

Section: Survey Of Other Review Papersmentioning

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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Distance‐based clustering of mixed data

Cited by 64 publications

References 54 publications

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results

Visualizing Inequality in Health and Socioeconomic Wellbeing in the EU: Findings from the SHARE Survey

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

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