Learnable Weighting of Intra-attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes

Zhang, Yiqun; Cheung, Yiu-ming

doi:10.1109/tpami.2021.3056510

Cited by 10 publications

(6 citation statements)

References 52 publications

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“…Other authors, such as S. Salem, S. Naouali, and Z. Chtourou, have also worked on rough-set clustering [64,[70][71][72]. Furthermore, Y. M. Cheung, as the second author, has proposed numerous methods related to distance metrics with Y. Q. Zhang [73][74][75] and H. Jia [58]. Moreover, F. L. Chen proposed variant methods for optimizing the objective function in subspace clustering algorithms [61,76,77].…”

Section: • Publication Titles and Publishersmentioning

confidence: 99%

“…Zhang & Cheung (2022) UDM [73] entropy-based distance metric using the weights attributes Distance measures: HD [184], Goodall [196], Lim [200], context-based distance metric (CBDM) [203], FPDM [58] and EBDM [75] are embedded into K-modes, entropy-based categorical data clustering (ECC) [182], the representative attribute weighting K-modes (WKM) [130], mixed attribute WKM (MWKM) [131], and SCC [61], and WOC [205] Zhang & Cheung (2022) HD-NDW [74] an Bolzmann's entropy [209] Distance Measure with Entropy (DME) [210], HD [211], Weighted Similarity Measure (WSM) [109], FPDM, Gambaryan [212], Burnaby [213], embedded with K-modes, weighted K-modes [176] and Density Peak Clustering for Mixed Data (DPC-MD) algorithms [210] Table 13. Cont.…”

Section: Ye Et Al (2019)mentioning

confidence: 99%

“…The HD-NDW algorithm [74], or homogeneous distance-novel distance weighting, is a clustering algorithm that incorporates HD intra-attribute information, focusing on the intrinsic connection between ordinal and nominal attributes. At the same time, the NDW calculates the weights of intra-attribute distances defined by HD to achieve optimal clustering results.…”

Section: • the Mixed Categorical Attributes (Nominal And Ordinal) Met...mentioning

confidence: 99%

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Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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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.

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Section: • Publication Titles and Publishersmentioning

confidence: 99%

Section: Ye Et Al (2019)mentioning

confidence: 99%

Section: • the Mixed Categorical Attributes (Nominal And Ordinal) Met...mentioning

confidence: 99%

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Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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“…An entropy-based distance metric for ordinal attributes is devised to estimate the distance between categories of an ordinal attribute, which utilizes the underlying order information. The next step is to generalize this distance measure and suggest a single one that applies to ordinal and nominal attribute categorical data [51].…”

Section: E a R L Y B I R Dmentioning

confidence: 99%

Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data

Kumar,

Kumar

2024

csci

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In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation and unification. Data augmentation techniques have proven effective in enhancing models' performance by expanding the dataset's size. We employ ML data augmentation, unification, and clustering techniques. First, we augment the \textit{limited} survey data size using data augmentation technique(s). Next, we carry out data unification, followed by clustering for inferencing. We took two benchmark survey datasets to demonstrate the effectiveness of augmentation and unification. One is on features of students to be entrepreneurs, and the second is breast cancer survey data. We compare the results of the inference obtained from the raw survey data and the newly converted data. The results of this study indicate that the machine learning approach, data augmentation with the unification of data followed by clustering, can be beneficial for generalizing the inferences drawn from the survey data.

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“…The prophet [14] is an open-source library by FB company made for predicting time series data to learn and likely forecast the exchange. Seasonality variations occur over a short duration and aren't notable enough to be described as a trend.…”

Section: Prophetmentioning

confidence: 99%

Experimental Performance Analysis of Machine Learning Algorithms

Khekare

Turukmane

Dhule

et al. 2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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Machine Learning models and algorithms have become quite common these days. Deep Learning and Machine Learning algorithms are utilized in various projects, and now, it has opened the door to several opportunities in various fields of research and business. However, identifying the appropriate algorithm for a particular program has always been an enigma, and that necessitates to be solved ere the development of any machine learning system. Let’s take the example of the Stock Price Prediction system, it is used to identify the future asset prediction of a industry or other financial aspects traded on a related transaction. Now, it is a daunting task to find the right algorithm or model for such a purpose that can predict accurate values. There are several other systems such as recommendation systems, sales prediction of a mega-store, or predicting what are the chances of a driver meeting an accident based on his past records and the road they’ve taken. These problem statements require to be built using the most suitable algorithm and identifying them is a necessary task. This is what the system does, it compares a set of machine learning algorithms while determining the appropriate algorithm for the selected predictive system using the required data sets. The objective is to develop an interface that can be used to display the result matrix of different machine learning algorithms after being exposed to different datasets with different features. Besides that, one can determine the most suitable (or optimal) models for their operations, using these fundamentals. For experimental performance analysis several technologies and tools are used including Python, Django, Jupyter Notebook, Machine Learning, Data Science methodologies, etc. The comparative performance analysis of best known five time series forecasting machine learning algorithms viz. linear regression, K – nearest neighbor, Auto ARIMA, Prophet, and Support Vector Machine is done. Stock market, earth and sales forecasting data is used for analysis.

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Learnable Weighting of Intra-attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes

Cited by 10 publications

References 52 publications

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data

Experimental Performance Analysis of Machine Learning Algorithms

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