Clustering Algorithm For Categorical Data Research Articles

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

The similarity of the similarity measures in the context of clustering algorithms for categorical data

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 analyzed data are binary, non-binary, or mixed. In the case of categorical data, it begins to matter how many attributes we have and how many different values they can take. Cluster analysis is an essential technique in unsupervised machine learning. Object-clustering cannot be determined without determining similarities between objects in the set and searching for clusters with the most significant internal consistency and external separatory. Therefore, we need knowledge about which measure behaves similarly and which differs depending on the nature of the analyzed data. According to our knowledge, there is no such research in the literature. In research, we analyzed six different measures for eight different datasets (differing in size and data type). The results say, among others, which measures take the longest time for calculations and which are the most correlated with others, so they can be used interchangeably without fear of losing valuable information about the mutual similarity of objects.

MIGR: A Categorical Data Clustering Algorithm Based on Information Gain in Rough Set Theory

Clustering techniques are used to split data into clusters where each cluster contains elements that look more similar to elements in the same cluster than elements in other clusters. Some of these techniques are capable of handling clustering process uncertainty, while other techniques may have stability issues. In this paper, a novel method, called Minimum Information Gain Roughness (MIGR), is proposed to select the clustering attribute based on information entropy with rough set theory. To evaluate its performance, three benchmark UCI datasets are chosen to be clustered by using MIGR. Then, the resulting clusters are compared to those which are resulted from applying Min-Min-Rough (MMR) and information-theoretic dependency roughness (ITDR) algorithms. Both last-mentioned techniques were already compared with a variety of clustering algorithms like k-modes, fuzzy centroids, and fuzzy k-modes. The Global purity, the overall purity, and F-measure are considered here as performance measures to compare the quality of the resulting clusters. The experimental results show that the MIGR algorithm outperforms both MMR and ITDR algorithms for clustering categorical data.

Improved minimum-minimum roughness algorithm for clustering categorical data

Clustering is a fundamental technique in data mining and machine learning. Recently, many researchers are interested in the problem of clustering categorical data and several new approaches have been proposed. One of the successful and pioneering clustering algorithms is the Minimum-Minimum Roughness algorithm (MMR) which is a top-down hierarchical clustering algorithm and can handle the uncertainty in clustering categorical data. However, MMR tends to choose the category with less value leaf node with more objects, leading to undesirable clustering results. To overcome such shortcomings, this paper proposes an improved version of the MMR algorithm for clustering categorical data, called IMMR (Improved Minimum-Minimum Roughness). Experimental results on actual data sets taken from UCI show that the IMMR algorithm outperforms MMR in clustering categorical data.

Self-Expressive Kernel Subspace Clustering Algorithm for Categorical Data with Embedded Feature Selection

Kernel clustering of categorical data is a useful tool to process the separable datasets and has been employed in many disciplines. Despite recent efforts, existing methods for kernel clustering remain a significant challenge due to the assumption of feature independence and equal weights. In this study, we propose a self-expressive kernel subspace clustering algorithm for categorical data (SKSCC) using the self-expressive kernel density estimation (SKDE) scheme, as well as a new feature-weighted non-linear similarity measurement. In the SKSCC algorithm, we propose an effective non-linear optimization method to solve the clustering algorithm’s objective function, which not only considers the relationship between attributes in a non-linear space but also assigns a weight to each attribute in the algorithm to measure the degree of correlation. A series of experiments on some widely used synthetic and real-world datasets demonstrated the better effectiveness and efficiency of the proposed algorithm compared with other state-of-the-art methods, in terms of non-linear relationship exploration among attributes.

Qualitative Data Clustering to Detect Outliers.

Detecting outliers is a widely studied problem in many disciplines, including statistics, data mining, and machine learning. All anomaly detection activities are aimed at identifying cases of unusual behavior compared to most observations. There are many methods to deal with this issue, which are applicable depending on the size of the data set, the way it is stored, and the type of attributes and their values. Most of them focus on traditional datasets with a large number of quantitative attributes. The multitude of solutions related to detecting outliers in quantitative sets, a large and still has a small number of research solutions is a problem detecting outliers in data containing only qualitative variables. This article was designed to compare three different categorical data clustering algorithms: K- algorithm taken from MacQueen’s K- algorithm and the and algorithms. The comparison concerned the method of dividing the set into clusters and, in particular, the outliers detected by algorithms. During the research, the authors analyzed the clusters detected by the indicated algorithms, using several datasets that differ in terms of the number of objects and variables. They have conducted experiments on the parameters of the algorithms. The presented study made it possible to check whether the algorithms similarly detect outliers in the data and how much they depend on individual parameters and parameters of the set, such as the number of variables, tuples, and categories of a qualitative variable.

Cloud deployment of game theoretic categorical clustering using apache spark: An application to car recommendation

Personal vehicles are invariably being preferred over public transport nowadays. Contact-less feature inspection and analysis based on personal preferences will be in high demand among customers in the post-pandemic world. A comprehensive online car recommendation system will be the customers’ spontaneous choice to understand and select the features of vehicles. However, the clustering of such categorical features is a challenging task as it is difficult to compare two textual attributes. In this paper, we have designed a cloud-based system that will automatically address this issue. Motivated by the cooperative game theory and fuzzy technique, and integrating the concept of Shapley theorem, a categorical data clustering algorithm has been developed. At the same time, to overcome the major limitation of having a high time complexity of the order O(n2) associated with the Shapley computation, the proposed algorithm has been distributed using Apache Spark’s Map Reduce architecture in Google Cloud Platform. The model has been thoroughly validated based on its performance on several synthetic as well as real data sets. Finally, a car recommendation system has been proposed and tested on three car sell data sets. The proposed approach outperforms the corresponding existing categorical clustering approaches in terms of various clustering validity indices. To the best of the authors’ knowledge, this is the first attempt to apply Map Reduce based Shapley computation over the categorical clustering, which can find its application beyond the proposed car recommendation system as well.

Система управления оценочными средствами

The program system of the management of assessment tools for educational process described in the article. The system reduces the time that the teacher spends on the preparation of test materials, allowing you to create a set of assessment documents in a semi-automatic mode. The system implements the ability to create and manage task banks. Each task in the bank has a set of attributes that are involved in the process of generating assessment documents. The system also implements the ability to automatically generate the required number of variants of the assessment documents. At the same time, the algorithm for generating a set of assessment documents works in such a way that, on the one hand, one set includes the most similar variants of assessment documents in structure, and on the other hand, each assessment document in the set is unique. The algorithm for generating a set of assessment documents is based on the clustering algorithm for categorical data. In this paper a modification of the CLOPE algorithm was submitted, which allows you to automatically determine the required number of clusters, depending on the input data. Also, this modification solves the problem of small clusters and the problem of categorical clustering of numerical attributes. The paper also describes an iterative algorithm for the generation of a set of assessment documents.

Detecting Community Structure in Scientific Network - A Citation Network Analysis for Scientific

The study of the underlying community structure in citation networks affords plentiful aids for the progress of the research field, with building a basis for forthcoming research through the credit of previous research activities; identifying gaps in research for students and researchers; improving the incorporation between practice and theory, and so on. In recent years, a growing numeral of clustering algorithms for categorical data has been introduced to learn the community structure of the scientiuc networks. However, many were ineffective for large scale data, thereby making it harder to visualize the community, itself. Additionally, one of the major limitations of the citation network related works is that, none of them provide a visualization of the community structure, which makes it harder for the researchers to perceive the connections. This paper aims at investigating the community structure of various subdivision fields in the citation network of articles published on different scientific journals. Here, a top-down agglomerative clustering algorithm (Louvain) is applied to reveal major communities that correspond to obviously discernable subfields of Science. The measure of modularity is used to define the power of the community structure, thereby demonstrating that the algorithm used is highly efficient and effective at discovering community structure in the citation network data.

Fuzzy rough clustering for categorical data

Unlabeled categorical data is common in many applications. Because there is no geometric structure for categorical data, how to discover knowledge and patterns from unlabeled categorical data is an important problem. In this paper, a fuzzy rough clustering algorithm for categorical data is proposed. The proposed algorithm uses the partition of each attribute to calculate the granularity of each attribute and introduces information granularity to measure the significance of each attribute. It is different from traditional clustering algorithms for categorical data that the proposed algorithm can transform categorical data set into numeric data set and introduces a nonlinear dimension reduction algorithm to decrease the dimensions of data set. The proposed algorithm and the comparison algorithms are executed on real data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the most data sets and the results prove that the proposed algorithm is an effective clustering algorithm for categorical data sets.

A method for k-means-like clustering of categorical data

Despite recent efforts, the challenge in clustering categorical and mixed data in the context of big data still remains due to the lack of inherently meaningful measure of similarity between categorical objects and the high computational complexity of existing clustering techniques. While k-means method is well known for its efficiency in clustering large data sets, working only on numerical data prohibits it from being applied for clustering categorical data. In this paper, we aim to develop a novel extension of k-means method for clustering categorical data, making use of an information theoretic-based dissimilarity measure and a kernel-based method for representation of cluster means for categorical objects. Such an approach allows us to formulate the problem of clustering categorical data in the fashion similar to k-means clustering, while a kernel-based definition of centers also provides an interpretation of cluster means being consistent with the statistical interpretation of the cluster means for numerical data. In order to demonstrate the performance of the new clustering method, a series of experiments on real datasets from UCI Machine Learning Repository are conducted and the obtained results are compared with several previously developed algorithms for clustering categorical data.

The application of the CLOPE algorithm for clustering a set of the network packets

For the modern software that uses network communication protocols the problem of the ensuring reliability is acute. To solve such an important problem the stress testing is used. This type of the testing involves the generation of a large number of the test data, including sets of network packets. Reducing the stored data after the testing process is the main task. This task can be solved by clustering the set of received packets. To solve this problem it is proposed to use the clustering algorithm for categorical data of CLOPE. This algorithm allows to cluster datasets without the information about the source clusters. It has the low computational complexity and ease of implementation. The article describes the preparation and results of sets of the network packets processing experiments. The article shows that the CLOPE algorithm can be effectively used for the clustering network packets received during stress testing. The results of the research extend the toolkit for the SW stress testing process.

Partition-and-merge based fuzzy genetic clustering algorithm for categorical data

Categorical data clustering is a difficult and challenging task due to the special characteristic of categorical attributes: no natural order. Thus, this study aims to propose a two-stage method named partition-and-merge based fuzzy genetic clustering algorithm (PM-FGCA) for categorical data. The proposed PM-FGCA uses a fuzzy genetic clustering algorithm to partition the dataset into a maximum number of clusters in the first stage. Then, the merge stage is designed to select two clusters among the clusters that generated in the first stage based on its inter-cluster distances and merge two selected clusters to one cluster. This procedure is repeated until the number of clusters equals to the predetermined number of clusters. Thereafter, some particular instances in each cluster are considered to be re-assigned to other clusters based on the intra-cluster distances. The proposed PM-FGCA is implemented on ten categorical datasets from UCI machine learning repository. In order to evaluate the clustering performance, the proposed PM-FGCA is compared with some existing methods such as k-modes algorithm, fuzzy k-modes algorithm, genetic fuzzy k-modes algorithm, and non-dominated sorting genetic algorithm using fuzzy membership chromosomes. Adjusted Ranked Index (ARI), Normalized Mutual Information (NMI), and Davies–Bouldin (DB) index are selected as three clustering validation indices which are represented to both external index (i.e., ARI and NMI) and internal index (i.e., DB). Consequently, the experimental result shows that the proposed PM-FGCA outperforms the benchmark methods in terms of the tested indices.

EGA-FMC: enhanced genetic algorithm-based fuzzy k-modes clustering for categorical data

Categorical data clustering is the unsupervised technique of grouping similar objects which have categorical attributes. We propose a genetic algorithm-based fuzzy k-modes categorical data clustering algorithm using multi-objective rank-based selection with enhanced elitism operation. Compactness of the clusters and inter-cluster separation were chosen as objectives to be optimised. During elitism, in every iteration, the best parent chromosomes were identified. The entire population was passed through the selection, crossover and mutation steps. The worst children were then replaced by the best parents. Our method was evaluated on three real-world datasets and resulted in clusters of better quality as compared to current methods with a significant reduction in computation time. Additionally, statistical significance tests were conducted to show the superiority of our approach over other clustering solutions.

Many-objective fuzzy centroids clustering algorithm for categorical data

Categorical data clustering algorithms, in contrast to numerical ones, are still in their infancy despite some algorithms have been proposed in the literature. It is known that many clustering algorithms are posed as optimization problems, where internal cluster validity functions are utilized as the objectives to find the optimal partitions. However, most of these methods consider a single criterion that can merely be applied to detect the particular structure/distribution of data. To overcome this issue, in this paper, a novel many objective fuzzy centroids clustering algorithms is proposed for categorical data using reference point based non-dominated sorting genetic algorithm, which simultaneously optimizes several cluster validity indices. In our work, an effective fuzzy centroids algorithm is employed to design the proposed approach, which is different from other contestant k-modes-type methods. Here, the fuzzy memberships are used for chromosome representation that combines with a novel genetic operation to produce new solutions. Moreover, a variable-length encoding scheme is developed for the sake of finding the clusters without knowing any prior knowledge. Experiments on several data sets demonstrate the superiority of the proposed algorithm over other state-of-the-art methods in terms of clustering accuracy and stability. On the other hand, our method can detect the cluster number if not predefined along with a desirable clustering solution.

CATCLUS – A Proposed Algorithm for Clustering Categorical Data

Classification of categorical data always involves more complexities compared to the numerical data. Because, a firm outline cannot be drawn in case of categorical data. Different types of assumptions are followed by various researchers to treat such kind of data. Again, dissimilarity measures applied in case of numerical data cannot be applied directly in this case. In this paper, a new clustering algorithm for categorical data is proposed. The algorithm is using a newly devised dissimilarity measure. This paper only includes the theoretical description of the proposed algorithm with appropriate example.

Term Frequency Based Cosine Similarity Measure for Clustering Categorical Data using Hierarchical Algorithm

Object in real world are categorical in nature. Categorical data are not analyzed as numerical data because of the absence of inherit ordering. In this study performance of cosine based hierarchical clustering algorithm for categorical data is evaluated. It make use of two functions such as Frequency Computation, Term Frequency based Cosine Similarity Matrix (TFCSM) computation. Clusters are formed using TFCSM based hierarchical clustering algorithm. Results are evaluated for vote real life data set using TFCSM based hierarchical clustering and standard hierarchical clustering algorithm using single link, complete link and average link method.

Improving CLOPE’s Profit Value and Stability with an Optimized Agglomerative Approach

CLOPE (Clustering with sLOPE) is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a different sequence. In this paper, a hierarchical clustering framework is proposed as an extension of CLOPE to generate stable and satisfactory clustering results based on an optimized agglomerative merge process. The new clustering profit is defined as the merge criteria and the cluster graph structure is proposed to optimize the merge iteration process. The experiments conducted on two datasets both demonstrate that the agglomerative approach achieves stable clustering results with a better profit value, but costs much more time due to the worse complexity.

A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

Detecting outliers in categorical data through rough clustering

Outlier detection is an important data mining task with many contemporary applications. Clustering based methods for outlier detection try to identify the data objects that deviate from the normal data. However, the uncertainty regarding the cluster membership of an outlier object has to be handled appropriately during the clustering process. Additionally, carrying out the clustering process on data described using categorical attributes is challenging, due to the difficulty in defining requisite methods and measures dealing with such data. Addressing these issues, a novel algorithm for clustering categorical data aimed at outlier detection is proposed here by modifying the standard $$k$$k-modes algorithm. The uncertainty regarding the clustering process is addressed by considering a soft computing approach based on rough sets. Accordingly, the modified clustering algorithm incorporates the lower and upper approximation properties of rough sets. The efficacy of the proposed rough $$k$$k-modes clustering algorithm for outlier detection is demonstrated using various benchmark categorical data sets.