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-modes algorithm taken from MacQueen’s K-means algorithm and the STIRR and ROCK 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.

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Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data

Algorithms ◽

10.3390/a11110177 ◽

2018 ◽

Vol 11 (11) ◽

pp. 177 ◽

Cited By ~ 2

Author(s):

Xuedong Gao ◽

Minghan Yang

Keyword(s):

Machine Learning ◽

Categorical Data ◽

Data Clustering ◽

Information Gain ◽

Clustering Algorithms ◽

Number Of Clusters ◽

Cluster Compactness ◽

Clustering Validation ◽

Categorical Data Clustering

Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and can act as the objective function of clustering algorithms. In this paper, we first studied several well-known internal CVIs for categorical data clustering, and proved the ineffectiveness of evaluating the partitions of different numbers of clusters without any inter-cluster separation measures or assumptions; the accurateness of separation, along with its coordination with the intra-cluster compactness measures, can notably affect performance. Then, aiming to enhance the internal clustering validation measurement, we proposed a new internal CVI—clustering utility based on the averaged information gain of isolating each cluster (CUBAGE)—which measures both the compactness and the separation of the partition. The experimental results supported our findings with regard to the existing internal CVIs, and showed that the proposed CUBAGE outperforms other internal CVIs with or without a pre-known number of clusters.

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K-modes Based Categorical Data Clustering Algorithms Satisfying Differential Privacy

2020 International Conference on Networking and Network Applications (NaNA) ◽

10.1109/nana51271.2020.00022 ◽

2020 ◽

Author(s):

Mingshuang Li ◽

Yihui Zhou ◽

Wenru Tang ◽

LaiFeng Lu

Keyword(s):

Categorical Data ◽

Data Clustering ◽

Differential Privacy ◽

Clustering Algorithms ◽

Categorical Data Clustering

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Generalized similarity measure for categorical data clustering

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) ◽

10.1109/icacci.2016.7732138 ◽

2016 ◽

Cited By ~ 1

Author(s):

Shruti Sharma ◽

Manoj Singh

Keyword(s):

Similarity Measure ◽

Categorical Data ◽

Data Clustering ◽

Categorical Data Clustering

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A Novel Cosine Similarity Like Data Clustering Method for Effective Data Classification in Data Mining

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.h6417.069820 ◽

2020 ◽

Vol 9 (8) ◽

pp. 340-346

Keyword(s):

Data Mining ◽

Similarity Measure ◽

Categorical Data ◽

Data Clustering ◽

Similarity Measures ◽

Numerical Data ◽

Data Classification ◽

Fundamental Goal ◽

Learning Technique ◽

Categorical Data Clustering

In data mining ample techniques use distance based measures for data clustering. Improving clustering performance is the fundamental goal in cluster domain related tasks. Many techniques are available for clustering numerical data as well as categorical data. Clustering is an unsupervised learning technique and objects are grouped or clustered based on similarity among the objects. A new cluster similarity finding measure, which is cosine like cluster similarity measure (CLCSM), is proposed in this paper. The proposed cluster similarity measure is used for data classification. Extensive experiments are conducted by taking UCI machine learning datasets. The experimental results have shown that the proposed cosinelike cluster similarity measure is superior to many of the existing cluster similarity measures for data classification.

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Improved minimum-minimum roughness algorithm for clustering categorical data

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2021.10.006 ◽

2021 ◽

Vol 8 (10) ◽

pp. 43-50

Author(s):

Truong et al. ◽

Keyword(s):

Machine Learning ◽

Data Mining ◽

Hierarchical Clustering ◽

Categorical Data ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Experimental Results ◽

Data Sets ◽

Top Down ◽

Hierarchical Clustering Algorithm

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.

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