检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Synergistic Effect Research of k-modes algorithm in the Evaluation of Physical Education Teaching 6

Synergistic Effect Research of K-modes Algorithm in the Eval...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

引用

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

来源：评论

学校读者我要写书评

暂无评论

On the impact of dissimilarity measure in k-modes clustering algorithm

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2007年第3期29卷 503-507页

作者： Ng, Michael k. Li, Mark Junjie Huang, Joshua Zhexue He, Zengyou Hong Kong Baptist Univ Dept Math Hong Kong Hong Kong Peoples R China Univ Hong Kong E Business Technol Inst Hong Kong Hong Kong Peoples R China Harbin Inst Technol Dept Comp Sci & Engn Harbin 150001 Peoples R China

This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k- modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k- modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.

关键词： data mining clustering k-modes algorithm categorical data

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Fuzzy clustering of categorical data using fuzzy centroids

引用

PATTERN RECOGNITION LETTERS 2004年第11期25卷 1263-1271页

作者： kim, DW Lee, kH Lee, D Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Biosyst Taejon 305701 South Korea Korea Adv Inst Sci & Technol Adv Informat Technol Res Ctr Taejon 305701 South Korea

In this paper the conventional fuzzy k-modes algorithm for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. To test the proposed approach, the proposed algorithm and two conventional algorithms (the k-modes and fuzzy k-modes algorithms) were used to cluster three categorical data sets. The proposed method was found to give markedly better clustering results. (C) 2004 Elsevier B.V. All rights reserved.

关键词： fuzzy clustering k-modes algorithm fuzzy k-modes algorithm categorical data fuzzy centroid

来源：评论

学校读者我要写书评

暂无评论

A new initialization method for categorical data clustering

引用

EXPERT SYSTEMS WITH APPLICATIONS 2009年第7期36卷 10223-10228页

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

In clustering algorithms, choosing a subset of representative examples is very important in data set. Such "exemplars" can be found by randomly choosing an initial subset of data objects and then iteratively refining it, but this works well only if that initial choice is close to a good solution. In this paper, based on the frequency of attribute values, the average density of an object is defined. Furthermore, a novel initialization method for categorical data is proposed, in which the distance between objects and the density of the object is considered. We also apply the proposed initialization method to k-modes algorithm and fuzzy k-modes algorithm. Experimental results illustrate that the proposed initialization method is superior to random initialization method and can be applied to large data sets for its linear time complexity with respect to the number of data objects. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Density Distance Initialization method Initial cluster center k-modes algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：