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Analysis of debt-paying ability of real estate enterprises based on fuzzy mathematics and k-means algorithm

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 6403-6414页

作者： Qiang, Qunli Anhui Jianzhu Univ Sch Econ & Management Hefei Anhui Peoples R China

Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the k-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalized additive model effectively reveals the complex nonlinear relationship between continuous predictors and house prices. Through research, it can be seen that the nonlinear generalized additive model based on machine learning can play an important role in real estate industry forecasting and has certain theoretical reference significance for subsequent related research.

关键词： Real estate generalized additive model machine learning k-means algorithm

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An improved k-means clustering algorithm for global earthquake catalogs and earthquake magnitude prediction

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JOURNAL OF SEISMOLOGY 2021年第3期25卷 1005-1020页

作者： Yuan, Rui Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China

The occurrences of earthquakes have no any explicit warning, and earthquake magnitude prediction is still extremely challenging now. Therefore, this study proposes a seismic prediction model based on clustering of global earthquake data. First, an improved k-means clustering algorithm for global earthquake catalogs is proposed. Traditional k-means clustering has several limitations, i.e., the number of clusters needs to be initialized, the initial cluster centers are arbitrarily selected, and there is currently no magnitude parameter in the k-means clustering algorithm. To improve the algorithm, this study employs the space-time-magnitude (STM) distance and then proposes a maximum-minimum STM distance for the selection of the initial cluster centers. Additionally, the sum of squares error, Davies-Bouldin index, Calinski-Harabasz index, and silhouette coefficient are applied to determine the number of clusters. Subsequently, a seismic prediction model based on the clustering result combined with an artificial neural network is presented. Application of the improved clustering algorithm to the global seismic catalog from 1900-2019 obtained from the United States Geological Survey reveals better clustering accuracy than the traditional k-means algorithm and is also effective for seismic risk analysis in the local region. Furthermore, the seismic prediction model based on the clustering result also exhibits good performance, which has practical significance and reference value for future predictions of earthquake magnitude.

关键词： Global earthquake clustering k-means algorithm Artificial neural network Earthquake magnitude Prediction

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A new case-deletion strategy for case-base maintenance based on k-means Clustering algorithm applied to medical data

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2021年第4期15卷 541-559页

作者： Djebbar, Akila Merouani, Hayet Farida Djellali, Hayet Badji Mokhtar Univ Comp Sci Dept LRI Lab SRF Equip Annaba Algeria Badji Mokhtar Univ Comp Sci Dept LRS Lab Annaba Algeria

Case-Based Reasoning (CBR) system maintenance is an important issue for current medical systems research. Large-scale CBR systems are becoming more omnipresent, with immense case libraries consisting of millions of cases. Case-Base Maintenance (CBM) is the implementation of the following policies allowing to revise the organization and/or the content (information content, representation field of application, or the implementation) of the Case Base (CB) to improve future thinking. Diverse case-base deletion and addition policies have been proposed which claim to preserve case-base competence. This paper presents a novel clustering-based deletion policy for CBM that exploits the k-means clustering algorithm. Thus, CBM becomes a central subject whose objective is to guarantee the quality of the CB and improve the performance of CBM. The proposed approach exploited clustering, which groups similar cases using the k-means algorithm. We rely on the characterization made of the different cases in the CB, and we find this characterization by a method based on a criterion of competence and performance. From this categorization, case deletion becomes obvious. This quality depends on the competence and performance of the CB. Test results show that the proposed deletion strategy improved the efficiency of the CB while preserving ***, its performance was 13% more reliable. The effectiveness of the proposed approach examined on the medical databases and its performance has been compared with the existing approaches on deletion policy. Experimental results are very encouraging.

关键词： Case-Based Reasoning (CBR) Case-Base Maintenance (CBM) deletion strategies clustering k-means algorithm performance competence

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RETRACTED: A study on e-commerce customer segmentation management based on improved k-means algorithm (Retracted Article)

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INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT 2020年第4期18卷 497-510页

作者： Deng, Yulin Gao, Qianying HoHai Univ Business Sch Nanjing Peoples R China

With the continuous popularization of the network, the customer resources have to be valued if enterprises want to occupy a certain share in the field of e-commerce. However, the traditional clustering analysis method has obvious lag for the segmentation of e-commerce customers. Therefore, accurate and efficient customer segmentation management should be carried out for the large and complex data information of current e-commerce enterprises, so as to realize customer retention and potential customer mining and promote the efficient development of enterprises. On the basis of customer segmentation theory, for the shortcomings of traditional k-means algorithm, a new SAPk + k-means algorithm based on semi-supervised Affinity Propagation combined with classic k-means algorithm is proposed in combination with AP algorithm, which is applied to e-commerce customers for segmentation management. The results show that when the SAPk + k-means algorithm clusters the iris dataset and the ionosphere dataset, the clustering time is longer than the k-means algorithm and the AP algorithm, but the algorithm error rate in the standard data is significantly reduced and the correct number of clusters can be obtained. The main steps of SAPk + k-means algorithm applied to customer segmentation management including data acquisition, cluster analysis and analysis and evaluation of clustering results. The SAPk + k-means algorithm clusters the data information of an e-commerce customer to obtain four different customer types and proposes corresponding strategies for each type of customer. It is concluded that the SAPk + k-means algorithm can significantly improve the clustering quality of customer data information and improve the effectiveness of activities of e-commerce enterprises.

关键词： k-means algorithm SAPk + k-means algorithm e-commerce Customer segmentation management

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A novel selective clustering framework for appropriate labeling of clusters based on k-means algorithm

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SCIENTIA IRANICA 2020年第5期27卷 2621-2634页

作者： Moslehi, F. Haeri, A. Gholamian, M. R. Iran Univ Sci & Technol Sch Ind Engn Tehran Iran

Data mining is a powerful new technology to extract hidden information from data warehouses. Data mining analyzes data from different perspectives and finds useful patterns and knowledge from large volumes of raw data. Clustering is one of the main methods of data mining. k-means algorithm is one of the most common clustering algorithms due to its efficiency and ease of use. One of the challenges of clustering is to identify the appropriate label for each cluster. The selection of a label is done so as to provide a proper description of cluster records. In some cases, choosing an appropriate label is not easy due to the results and structure of each cluster. The aim of this study is to present an algorithm based on the k-means clustering in order to facilitate the allocation of labels to each cluster. (C) 2020 Sharif University of Technology. All rights reserved.

关键词： Machine learning Data mining Clustering k-means algorithm Labeling of the clusters

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Illumination Compensation Model with k-means algorithm for Detection of Pavement Surface Cracks with Shadow

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2020年第1期34卷

作者： Ju Huyan Li, Wei Tighe, Susan Deng, Ranran Yan, Shuai Univ Waterloo Dept Civil & Environm Engn Ctr Pavement & Transportat Technol Waterloo ON N2L 3G1 Canada Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China

A small amount of research has been conducted in dealing with pavement shadow-affected cracks detection. Hence, this research aims to develop the illumination compensation model (ICM) and k-means clustering algorithm-based crack detection considering the influence of pavement shadows. First, the shadow area was divided into the umbra area and the penumbra area according to the illumination mechanism. Then, the shadow removal methods for different areas were analyzed separately. Since the intensity of the umbra shadow area changes homogeneously, the ICM approach can be a convenient way for shadow removal. While the intensity of penumbra area changes drastically, the cubic sample interpolation operation was conducted in advance, followed by ICM to finalize the shadow removal. After that, the k-means clustering algorithm was used to extract the crack region from the road background. Finally, based on the segmented binary crack image, the orientation, crack length, width, aspect ratio, area, and blocks were calculated for comprehensive crack-type classification and severity evaluation. Experiments were conducted to compare the performance of the proposed approach with traditional threshold segmentation, Poisson equation, contourlet transformation, and CrackTree, which demonstrated optimistic performance of the proposed method in terms of average precision (93.58%), recall (94.15%), and F-measure (93.86%). (c) 2019 American Society of Civil Engineers.

关键词： Pavement shadow Pavement crack detection Illumination compensation model (ICM) k-means algorithm

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An Improved k-means algorithm for Test Case Optimization 4

An Improved K-means Algorithm for Test Case Optimization

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4th IEEE International Conference on Computer and Communication Systems (ICCCS)

作者： Tan, Tian-Tian Wang, Bao-Sheng Tang, Yong Zhou, Xu Natl Univ Def Technol Dept Comp Changsha Peoples R China

ISBN: (纸本)9781728113227

In order to optimize the effectiveness and efficiency of software test cases, this paper proposed an improved k-means algorithm for test case optimization, introduced Degree of Membership Function to improve k-means algorithm to design a fuzzy clustering method, and combined the test requirements set, extracted test cases from each cluster, found similar test cases as more as possible. Experimental results showed that this algorithm can minimize the redundant test case set, keep the widest coverage at the same time, and has higher effectiveness and efficiency.

关键词： Software Test k-means algorithm Degree of Membership Function

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Energy efficient grid based k-means clustering algorithm for large scale wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第14期35卷 e5255-e5255页

作者： Ben Gouissem, Bechir Gantassi, Rahma Hasnaoui, Salem Univ Tunis El Manar Natl Engn Sch Tunis Commun Syst Lab SysCom BP 37 Tunis 1002 Tunisia

Hierarchical based routing is a kind of group-based routing that consists in creating of a virtual hierarchy among network sensor nodes. This class of routing technique is generally designed for large-scale networks. It aims to efficiently increase network lifetime by cutting the whole network into clusters. However, traditional clustering techniques show some limitations and do not take into consideration the self-organized with dynamic topology inherent in wireless sensor networks (WSNs). These limitations can lead to an unbalanced cluster head distribution that affects the whole network energy consumption. In this paper, we propose a grid-based k-means clustering protocol (named GBk), which combines grid-based routing with k-means algorithm in order to overcome the above mentioned weaknesses. From the supervised zone area size parameter, the base station determines the optimal grid size based on our optimization study. Afterwards, the k-means algorithm is executed in every grid cell generating a cluster head per cell grid where the nearest node to the grid cell centroid is elected. An enhancement of this proposed GBk algorithm named GBk-R is also proposed to extend network stability of the GBk algorithm by node scoring calculation that take as parameters the node remaining energy in addition to the distance to centroid. Our proposed GBk and GBk-R algorithms allow for an enhanced network stability and increase the network lifetime as demonstrated by our performance evaluation study. In addition, this GBk clustering algorithm provides a better network topology control and a better control of the random nature of node distribution by generating cluster heads with bounded localization.

关键词： clustering algorithm energy efficient algorithm grid-based algorithm k-means algorithm large scale WSN machine learning

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Application test of PCA based improved k-means clustering algorithm in analyzing NGO assistance needs in less developed countries

Application test of PCA based improved K-means clustering al...

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作者： Linhui Qin Department of Accounting and Finance Middlesex University London

In today's society where the amount of data is increasing by Peta Byte(PB) or Exa Byte(EB), it is an era of big data explosion, but there are also some unlabeled data or unstructured data. Compared with complex supervised learning, unmarked unsupervised learning has great potential and value in social development. The clustering algorithm k-means is one of the commonly used algorithms in unsupervised learning. However, after studying the shortcomings of k-means itself, a problem is found that the dimension attribute of the data set must be converted into a numeric type by means of arithmetic average to measure the *** random selection will have a certain degree of influence on the final clustering results,and eventually lead to the decision deviation is too large. Especially for high noise points,multidimensional, nonlinear social big data. In order to solve this problem, the theme of this paper is the application test of PCA based improved k-means clustering algorithm in analyzing NGO assistance needs in less developed countries. First, read and clean up the national data of 167 less developed countries. Secondly, data visualization and data preparation are carried out to re-scale. The principal component analysis algorithm is used to analyze and deal with *** trends are analyzed by combining a k-means model determined by scores obtained from the Hopkins statistical test with a list of countries ultimately in need of assistance. Finally,it can be tested that PCA data cleaning can effectively reduce data noise and improve the clustering effect.

关键词： PCA k-means algorithm data mining visualization Hopkins statistics

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The Application of Big Data Mining Prediction Based on Improved k-means algorithm 34

The Application of Big Data Mining Prediction Based on Impro...

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34th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Qiao, Yuchen Li, Yunlu Lv, Xiaotian Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781728139364

In order to solve the problem of low efficiency of k-means algorithm in processing the data mining prediction problem of big data with more attributes, an annual income prediction method of residents based on improved k-means algorithm is proposed. The improved k-means algorithm combines the principal component analysis method with the traditional k-means algorithm. After reducing the dimensionality of various data attributes, the data are classified with k-means algorithm. The research makes use of 1994 U.S. census database and conducts a contrastive analysis of the two algorithms. The results show that the prediction accuracy has been significantly improved by 13.3313%, from 53.1016% to 66.4329% It is clear the improved algorithm can effectively improve the accuracy of clustering and annual income prediction.

关键词： k-means algorithm Various attributes Data mining Principal component analysis Annual income forecast

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