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6th International Symposium on Computer Science and Intelligent Control (ISCSIC)

作者： Wang, Xue Xinjiang Univ Bldg 7Hualing Mansion102 Kunlun East St Urumqi Xinjiang Peoples R China

ISBN: (纸本)9781665454889

In order to improve the efficiency and accuracy of evaluation of physical education teaching, a physical education teaching evaluation method based on improved k-modes was proposed. Firstly, the minimum sum of the squared errors method and similarity calculation method were used to improve the number of cluster families, the initial cluster center and the distance measure method, so as to improve the traditional k-modes clustering. Then, the ratio of intra-class average distance and inter-class average distance was used as the evaluation index to evaluate the proposed algorithm. The results showed that compared with other algorithms, the proposed improved k-modes algorithm can best cluster teachers into three categories. In the actual evaluation test, the ratio of k-modes improved algorithm is 0.2302, the ratio of "random+0-1 matching method" and "SSE+ similarity calculation method" is 0.7241 and 0.3497, respectively. It shows that the improved k-modes algorithm has better clustering effect and more effective effect on physical education teaching evaluation.

关键词： k-modes algorithm evaluation of physical education teaching cluster center students' teaching evaluation data

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A Questionnaire Data Clustering Method Based on Optimized k-modes algorithm 2nd

A Questionnaire Data Clustering Method Based on Optimized K-...

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2nd International Conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 23rd International Conference on Human-Computer Interaction (HCII)

作者： Hou, Wen-jun Liu, Jia-xin Yan, Xiang-yuan Beijing Univ Posts & Telecommun Sch Digital Media & Design Arts Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst & Network Culture Beijing 100876 Peoples R China

ISBN: (纸本)9783030777715;9783030777722

When conducting user research, questionnaires are often used to collect user characteristics, attitudes, and other information. Data cluster analysis is often used to divide user groups. The traditional clustering algorithms are mostly only suitable for numerical attributes or disordered categorical attributes. However, questionnaire data is mainly composed of disordered and ordinal categorical attributes. To solve the questionnaire data clustering problem, based on the traditional k-modes algorithm, a method that combines subjective weighting and objective clustering for questionnaire data analysis is proposed. This method first performs multiple-choice clustering questions to reduce dimensionality and then re-weighting ordinal categorical attributes to rationalize the distance measurement. An optimized mixed k-modes algorithm for questionnaire data clustering is proposed. The dissimilarity measure between objects according to the two types of disorder and ordinal categorical attributes. In order to evaluate the clustering results, an effective cluster validity index is also defined in this paper. Using a bank user survey questionnaire as a case proved the effectiveness of this data clustering method.

关键词： Questionnaire data clustering k-modes algorithm Mixed categorical data Ordinal categorical data weighting

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APPLIED INTELLIGENCE 2020年第5期50卷 1498-1509页

作者： Yuan, Fang Yang, Youlong Yuan, Tiantian Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Northwestern Polytech Univ Sch Management Xian 710071 Peoples R China

Among the existing clustering algorithms, the k-Means algorithm is one of the most commonly used clustering methods. As an extension of the k-Means algorithm, the k-modes algorithm has been widely applied to categorical data clustering by replacing means with modes. However, there are more mixed-type data containing categorical, ordinal and numerical attributes. Mixed-type data clustering problem has recently attracted much attention from the data mining research community, but most of them fail to notice the ordinal attributes and establish explicit metric similarity of ordinal attributes. In this paper, the limitations of some existing dissimilarity measure of k-modes algorithm in mixed ordinal and nominal data are analyzed by using some illustrative examples. Based on the idea of mining ordinal information of ordinal attribute, a new dissimilarity measure for the k-modes algorithm to cluster this type of data is proposed. The distinct characteristic of the new dissimilarity measure is to take account of the ordinal information of ordinal attribute. A convergence study and time complexity of the k-modes algorithm based on this new dissimilarity measure indicates that it can be effectively used for large data sets. The results of comparative experiments on nine real data sets from UCI show the effectiveness of the new dissimilarity measure.

关键词： Clustering algorithm Mixed-type data k-modes algorithm Dissimilarity measure of ordinal attribute

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An Improved k-modes Clustering algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure

An Improved K-modes Clustering Algorithm Based on Intra-clus...

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2nd International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2017)

作者： Hongfang Zhou Yihui Zhang Yibin Liu School of Computer Science and Engineering Xi'an University of Technology

ISBN: (纸本)9781510845008

Categorical data clustering has attracted much attentions recently because most practical data contains categorical attributes. The k-modes algorithm, as the extension of the k-means algorithm, is one of the most widely used clustering algorithms for categorical data. In this paper, we firstly analyzed the limitations of two existing dissimilarity measures. Based on this, we proposed a novel dissimilarity measure--IID. IID considers the relationship between the object and all clusters as well as that within clusters. Finally the experiments are made on six benchmark data sets from UCI. And the corresponding results show that IID achieves better performance than two existing ones used in k-modes and kBGRD algorithms.

关键词： Clustering categorical data dissimilarity measure k-modes algorithm

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Identifying clusters of innovation barriers in farming enterprises: a k-modes clustering approach

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AGREkON 2025年第1期64卷 32-49页

作者： Buchana, Yasser Human Sci Res Council City Ctr Sci Technol & Innovat Indicators Cape Town South Africa

Despite the relevance of agriculture in terms of its contribution to food security and environmental sustainability, empirical evidence on the nature and effects of barriers to innovation, remains very limited. This problem has often led to inconsistent policy design, failing to meet the needs and expectations of farming enterprises. Innovation profiling and segmentation have emerged as important tools for understanding how businesses innovate. Using data from the South African Agricultural Business Innovation Survey (AgriBIS-2016-2018), this study applies the k-modes clustering algorithm, to group farming enterprises based on the innovation barriers they face. The analysis identified three distinct clusters with differing innovation profiles. The findings show that businesses in Cluster 2 recognised institutional barriers such as Lack of Government Support (65.4%) and Stringent Agricultural Policies (93.6%) as key barriers to their innovations, while Cluster 3 businesses highlighted environmental factors as critical impediments to their innovation. Cluster 1 businesses faced a diverse range of resource-related barriers. This study addresses a key knowledge gap and increases our understanding of the barriers to agricultural innovation. The findings have important implications for innovation policy instruments and strategies aimed at promoting agricultural innovation, sustainability, productivity and resilience in the face of barriers to innovation.

关键词： Cluster analysis k-modes algorithm machine learning innovation barriers agriculture

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The role of the O2O blended teaching model in improving the teaching effectiveness of physical education classes

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Qiao, Honghui Henan Vocat Univ Sci & Technol Publ Basic Educ Dept Zhoukou 466000 Peoples R China

The deep fusion of Internet technology and education is constantly pushing forward the reform of university education. Traditional educational ideas, concepts, and models cannot keep pace with the times, and hybrid teaching has become a new way of education in colleges and universities. To improve the teaching effect of physical education classes, the study used a blended teaching model and designed a teaching evaluation and performance prediction model under the blended teaching model based on an improved cluster analysis method and attention mechanism. The lab results indicated that under the blended teaching model, students' performance increased by 12.89 points, and the level of skill mastery and proficiency increased by 26.52 and 28.55%, respectively, with grades more inclined to high score distribution. "Excellent" grade clustering increased by 77.71%, and "Good" grade clustering increased by 19.01%. The minimum error sum of squares of the improved clustering algorithm was 58.18 and 36.25% lower than the other two algorithms, and the clustering results were more relevant. The two-way attention mechanism algorithm predicted higher accuracy results and performed best on all four evaluation metrics, with a prediction accuracy of 98.23%, an accuracy of 98.42%, and an F1 value of 91.78%. This hybrid teaching model is more in line with the characteristics of the physical education teaching discipline, successfully cultivates students' independent learning ability, stimulates students' love for physical education courses, and achieves better teaching results.

关键词： O2O blended learning k-modes algorithm predictive model evaluation metrics

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A weighting k-modes algorithm for subspace clustering of categorical data

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NEUROCOMPUTING 2013年 108卷 23-30页

作者： Cao, Fuyuan Liang, Jiye Li, Deyu Zhao, Xingwang Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat Minist Educ Taiyuan 030006 Shanxi Peoples R China

Traditional clustering algorithms consider all of the dimensions of an input data set equally. However, in the high dimensional data, a common property is that data points are highly clustered in subspaces, which means classes of objects are categorized in subspaces rather than the entire space. Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a data set. In this paper, a weighting k-modes algorithm is presented for subspace clustering of categorical data and its corresponding time complexity is analyzed as well. In the proposed algorithm, an additional step is added to the k-modes clustering process to automatically compute the weight of all dimensions in each cluster by using complement entropy. Furthermore, the attribute weight can be used to identify the subsets of important dimensions that categorize different clusters. The effectiveness of the proposed algorithm is demonstrated with real data sets and synthetic data sets. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Subspace clustering Weight k-modes algorithm Categorical data

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Improved Clustering for Categorical Data with Genetic algorithm 1st

Improved Clustering for Categorical Data with Genetic Algori...

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1st International Conference on Microelectronics, Computing & Communication Systems (MCCS)

作者： Sharma, Abha Thakur, R. S. Maulana Azad Natl Inst Technol Bhopal India

ISBN: (纸本)9789811055652;9789811055645

Clustering is the most significant unsupervised learning where the aim is to partition the data set into uniform groups called clusters. Many real-world data sets often contain categorical values, but many clustering algorithms work only on numeric values which limits its use in data mining The k-modes algorithm is one of the very effective for proper partitions of categorical data sets, though the algorithm stops at locally optimum solution as depended on initial cluster centres. Proposed algorithm utilizes the genetic algorithm (GA) to optimize the k-modes clustering algorithm. The reason is, considering noise as cluster centres gives the high cost which will not fit for the next iteration and also not gets stuck to the suboptimal solutions. The superiority of proposed algorithm is demonstrated for several real-life data sets in terms of accuracy and proves it is efficient and can reveal encouraging results especially for the large datasets.

关键词： Clustering Categorical data Genetic algorithm k-modes algorithm

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Research on Improvement of Text Processing and Clustering algorithms in Public Opinion Early Warning System 5

Research on Improvement of Text Processing and Clustering Al...

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5th International Conference on Systems and Informatics (ICSAI)

作者： Yang, kongyu Miao, Ruijie Beijing Informat Sci & Technol Univ Sch Publ Adm & Commun Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Informat Management Beijing Peoples R China

ISBN: (纸本)9781728101200

In order to provide the necessary data for Public opinion monitoring and trend warning, this paper did some researches on text processing and clustering algorithms based on hot topics of the Weibo. Data that get from Weibo were classification data which contain two properties. To adapt this feature and meet the requirement of public opinion trends warning, hamming distance was used to do text similarity computing. By improving the traditional k-means algorithm, a new k-mode algorithm which is used to text clustering on hot topics was achieved. Simulation and results analysis indicated the text processing method was accurate and suitable to the microblog public opinion early warning.

关键词： Public opinion early warning Text processing Hamming distance Text clustering k-modes algorithm

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kNOWLEDGE-BASED SYSTEMS 2012年 26卷 120-127页

作者： Cao, Fuyuan Liang, Jiye Li, Deyu Bai, Liang Dang, Chuangyin Shanxi Univ Sch Comp & Informat Technol Minist Educ Key Lab Computat Intelligence & Chinese Informat Taiyuan 030006 Shanxi Peoples R China City Univ Hong Kong Dept Mfg Engn & Engn Management Hong Kong Hong Kong Peoples R China

Clustering is one of the most important data mining techniques that partitions data according to some similarity criterion. The problems of clustering categorical data have attracted much attention from the data mining research community recently. As the extension of the k-Means algorithm, the k-modes algorithm has been widely applied to categorical data clustering by replacing means with modes. In this paper, the limitations of the simple matching dissimilarity measure and Ng's dissimilarity measure are analyzed using some illustrative examples. Based on the idea of biological and genetic taxonomy and rough membership function, a new dissimilarity measure for the k-modes algorithm is defined. A distinct characteristic of the new dissimilarity measure is to take account of the distribution of attribute values on the whole universe. A convergence study and time complexity of the k-modes algorithm based on new dissimilarity measure indicates that it can be effectively used for large data sets. The results of comparative experiments on synthetic data sets and five real data sets from UCI show the effectiveness of the new dissimilarity measure, especially on data sets with biological and genetic taxonomy information. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Categorical data clustering k-modes algorithm Rough membership function Dissimilarity measure Genetic taxonomy

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