Distance Metrics and Clustering Methods for Mixed‐type Data

Foss, Alexander H.; Markatou, Marianthi; Ray, Bonnie K.

doi:10.1111/insr.12274

Cited by 50 publications

(45 citation statements)

References 123 publications

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“…Further, as input, we used a dataset with no preprocessing. These distance metrics serve to capture the differences between the data samples and vary in their capacity to deal with large outliers (i.e., between weighted, centroid, and median metrics) or if they allow choosing the number of clusters to consider (e.g., Ward) (Foss, Markatou & Ray, 2019). After this clustering, we tested all of the datasets created in the previous step to determine the best preprocessing methodology.…”

Section: Unsupervised Learning Experimentsmentioning

confidence: 99%

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data

Tabares-Soto

Orozco-Arias

Romero-Cano

et al. 2020

PeerJ Computer Science

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Cancer classification is a topic of major interest in medicine since it allows accurate and efficient diagnosis and facilitates a successful outcome in medical treatments. Previous studies have classified human tumors using a large-scale RNA profiling and supervised Machine Learning (ML) algorithms to construct a molecular-based classification of carcinoma cells from breast, bladder, adenocarcinoma, colorectal, gastro esophagus, kidney, liver, lung, ovarian, pancreas, and prostate tumors. These datasets are collectively known as the 11_tumor database, although this database has been used in several works in the ML field, no comparative studies of different algorithms can be found in the literature. On the other hand, advances in both hardware and software technologies have fostered considerable improvements in the precision of solutions that use ML, such as Deep Learning (DL). In this study, we compare the most widely used algorithms in classical ML and DL to classify the tumors described in the 11_tumor database. We obtained tumor identification accuracies between 90.6% (Logistic Regression) and 94.43% (Convolutional Neural Networks) using k-fold cross-validation. Also, we show how a tuning process may or may not significantly improve algorithms’ accuracies. Our results demonstrate an efficient and accurate classification method based on gene expression (microarray data) and ML/DL algorithms, which facilitates tumor type prediction in a multi-cancer-type scenario.

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Section: Unsupervised Learning Experimentsmentioning

confidence: 99%

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data

Tabares-Soto

Orozco-Arias

Romero-Cano

et al. 2020

PeerJ Computer Science

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“…For a thorough review of model‐based clustering methods for mixed‐type data we refer the reader to Foss et al ().…”

Section: Discussionmentioning

confidence: 99%

Distance‐based clustering of mixed data

Velden

D’Enza

Markos

2018

WIREs Computational Stats

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Cluster analysis comprises of several unsupervised techniques aiming to identify a subgroup (cluster) structure underlying the observations of a data set. The desired cluster allocation is such that it assigns similar observations to the same subgroup. Depending on the field of application and on domain‐specific requirements, different approaches exist that tackle the clustering problem. In distance‐based clustering, a distance metric is used to determine the similarity between data objects. The distance metric can be used to cluster observations by considering the distances between objects directly or by considering distances between objects and cluster centroids (or some other cluster representative points). Most distance metrics, and hence the distance‐based clustering methods, work either with continuous‐only or categorical‐only data. In applications, however, observations are often described by a combination of both continuous and categorical variables. Such data sets can be referred to as mixed or mixed‐type data. In this review, we consider different methods for distance‐based cluster analysis of mixed data. In particular, we distinguish three different streams that range from basic data preprocessing (where all variables are converted to the same scale), to the use of specific distance measures for mixed data, and finally to so‐called joint data reduction (a combination of dimension reduction and clustering) methods specifically designed for mixed data. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Dimension Reduction

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“…The procedure to transform the input HTML files into weighted graphs was as follows: each DOM node was transformed into a graph node; the graph nodes were connected according to the links of their corresponding DOM nodes; the weights were computed by measuring the distance between the attributes of the corresponding nodes. Since the DOM nodes have real‐valued, categorical, and ordinal attributes, we resorted to Foss et al's 22 approach to compute the distances among them.…”

Section: Experimental Analysismentioning

confidence: 99%

On the synthesis of metadata tags for HTML files

et al. 2020

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Summary RDFa, JSON‐LD, Microdata, and Microformats allow to endow the data in HTML files with metadata tags that help software agents understand them. Unluckily, there are many HTML files that do not have any metadata tags, which has motivated many authors to work on proposals to synthesize them. But they have some problems: the authors either provide an overall picture of their designs without too many details on the techniques behind the scenes or focus on the techniques but do not describe the design of the software systems that support them; many of them cannot deal with data that are encoded using semistructured formats like forms, listings, or tables; and the few proposals that can work on tables can deal with horizontal listings only. In this article, we describe the design of a system that overcomes the previous limitations using a novel embedding approach that has proven to outperform four state‐of‐the‐art techniques on a repository with randomly selected HTML files from 40 different sites. According to our experimental analysis, our proposal can achieve an F1 score that outperforms the others by 10.14%; this difference was confirmed to be statistically significant at the standard confidence level.

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Distance Metrics and Clustering Methods for Mixed‐type Data

Cited by 50 publications

References 123 publications

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data

Distance‐based clustering of mixed data

On the synthesis of metadata tags for HTML files

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