Data quality is crucial in modern software systems, like data-driven decision support systems. However, data quality is affected by data anomalies, which represent instances that deviate from most of the data. These anomalies affect the reliability and ...

The purpose of the article is to develop a new dimensionality reduction algorithm for categorical data. We give a new geometric formulation of the PCA dimensionality reduction method for numerical data that can be effectively transferred to the ...

This article provides an overview of latent class analysis (LCA) and its applications in the field of health sciences. LCA is a statistical method used to identify unobserved subpopulations, or latent classes, within a population based on a set of ...

Abstract—The paper considers issues in presenting and assessing the quality of scientific data in the materials science domain. Problems with numerical and non-numerical data of various types are presented using examples of data on the properties of ...

This research aims to assess the similarity between determining the value of the similarity of objects by using various measures. Particularly for categorical data, the used similarity measures may behave differently depending on whether the ...

Multinomial probit models are routinely-implemented representations for learning how the class probabilities of categorical response data change with p observed predictors. Although several frequentist methods have been developed for estimation, inference ...

Most of real-world datasets are of mixed type including both numeric and categorical attributes. Unlike numbers, operations on categorical values are limited, and the degree of similarity between distinct values cannot be measured directly. In ...

In the fashion industry where social media has a growing presence, it is increasingly important to find the emergence of people's new tastes in the early stage based on the photos posted there. However, the amount of photos posted on fashion social ...

Missing data is a serious issue in software engineering because it can lead to information loss and bias in data analysis. Several imputation techniques have been proposed to deal with both numerical and categorical missing data. However, most of ...

One of the most popular, and versatile, ways of visually analyzing the associating between categorical data is to perform a correspondence analysis on the contingency table that is formed from their cross‐classification. Traditionally the analysis of ...

We briefly explore the various ways in which the association between the variables of a multiway contingency table can be visualized using correspondence analysis image image

Unavoidable noise in real-world categorical data presents significant challenges to existing outlier detection methods because they normally fail to separate noisy values from outlying values. Feature subspace-based methods inevitably mix noisy values ...

In the field of data mining, the analysis of categorical data (i.e., data that can assume a limited number of values, such as names) is particularly challenging due to the lack of implicit geometrical properties. Clustering of categorical data is ...

This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data. ...

Currently, the information is available in the organizations to gain more utilities together with some reason such as improving the strategy of organization, research, or etc.,. For this reason, the data holder cannot avoid to share some existing data-...

Clustering high-dimensional data is a challenging task in data mining, and clustering high-dimensional categorical data is even more challenging because it is more difficult to measure the similarity between categorical objects. Most algorithms assume ...

In this paper, we propose NPC_c, a new Naïve Possibilistic Classifier for categorical data. The proposed classifier relies on the Bayesian structure of the Naïve Bayes Classifier for categorical data (NBC_c) which stands for an interesting pattern when ...

Mining the interesting association rules from data and clusters presents high demands in many application fields, such as telephonic operator, social networks, marketing especially in decision-making process. Exploring the set of extracted rules from ...

In this article we discuss the techniques available to represent categorical data in Parallel Coordinates, a widely used visualisation method for multivariate datasets analysis tasks. We propose Parallel Bubbles, a frequency-based method improving the ...

A distance measure between objects is a key requirement for many data mining tasks like clustering, classification or outlier detection. However, for objects characterized by categorical attributes, defining meaningful distance measures is a challenging ...

This paper provides exploratory analysis on gaming statistics via various multivariate and categorical data analysis approaches. The clustering results show that the principal components associated with the gaming data are related to player expertise ...