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检索条件"主题词=k-Means algorithm"

共 1246 条记录，以下是111-120 订阅

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Curve Reconstruction of Digitized Surface using k-means algorithm

Curve Reconstruction of Digitized Surface using K-means Algo...

引用

24th DAAAM International Symposium on Intelligent Manufacturing and Automation

作者： kumar, Atul Jain, P. k. Pathak, P. M. Indian Inst Technol Dept Mech & Ind Engn Roorkee 247667 Uttar Pradesh India

The k-means algorithm has good ability to handle the large number of scanned data. It is best suited for creation a desired shape curve-likes shape for near-best approximation of the scanned data point set. It presented an approximate set of scanned data points with a simple curves or surfaces. In this paper, the reverse engineering use for scanning the spur gear using 3D laser scanner and data is stored in point cloud format. Using this scanned data, reconstructing the smooth curve is achieved by proposed algorithm in MATLAB environment. In this study, scanned geometry and curve reconstruction technique suggested and it has been demonstrated to tooth curve reconstruction of spur gear as a complex surface object. Result of methodology is helpful to recreate the 3D CAD model of scanned object, which can be improve in work efficiency and reduce the product development cycle time with the application of CAD/CAE/CAM tools. (C) 2014 The Authors. Published by Elsevier Ltd.

关键词： k-means algorithm reverse engineering digitized surface spur gear tooth surface curve reconstruction

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Segmentation of Thyroid Nodules with k-means algorithm on Mobile Devices 16

Segmentation of Thyroid Nodules with K-Means Algorithm on Mo...

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16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI)

作者： Tuncer, Seda Arslan Alkan, Ahmet Firat Univ Dept Software Engn Fac Engn TR-23119 Elazig Turkey Kahramanmaras Sutcu Imam Univ Dept Elect Elect Engn Fac Engn Kahramanmaras Turkey

ISBN: (纸本)9781467385206

Thyroid nodules are among the most common endocrine complaints in the world. Modern imaging techniques such as ultrasound (US), computerized tomography (CT), and magnetic resonance imaging (MRI) have revealed more thyroid nodules incidentally. Therefore segmentation and quantification of them are very important for the treatment. In this paper, we have proposed a medical image processing application that can be accessed over internet by android based mobile devices. The proposed system can be thought as a decision/diagnosis support system for physicians. The aim the study is the segmentation and quantification of the crossectional areas of the thyroid nodules. The analysis results are generated and compared with the manually segmented nodule areas by using Zijdenbos Similarity Index (ZSI) on a server. The android based mobile devices are employed as clients to reach the related medical data and analysis results over internet. Obtained ZSI values have shown that proposed methodology can be used as a decision support tool for thyroid nodule evaluation with average 90% ZSI segmentation accuracy. The obtained results showed that proposed methodology will contribute to the medical image processing issues on mobile devices and help physicians to access the system via mobile devices.

关键词： Throid Nodule Image Segmentation k-means algorithm Mobile Device

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Enhancing k-means algorithm for Solving Classification Problems

Enhancing K-means Algorithm for Solving Classification Probl...

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10th IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Thammano, Arit kesisung, Pannee King Mongkuts Inst Technol Ladkrabang Computat Intelligence Lab Fac Informat Technol Bangkok 10520 Thailand

ISBN: (纸本)9781467355605

k-means is the most popular clustering algorithm because of its efficiency and superior performance. However, the performance of k-means algorithm depends heavily on the selection of initial centroids. This paper proposes an extension to the original k-means algorithm enabling it to solve classification problems. First, the entropy concept is employed to adapt the traditional k-means algorithm to be used as a classification technique. Then, to improve the performance of k-means algorithm, a new scheme to select the initial cluster centers is proposed. The proposed models are tested on seven benchmark data sets from the UCI machine learning repository. Experimental results have shown that the proposed models outperform the learning vector quantization network in most of the tested data sets.

关键词： Data mining k-means algorithm Classsification Entropy

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Cluster Center Initialization Method for k-means algorithm Over Data Sets with Two Clusters

Cluster Center Initialization Method for <i>K</i>-means Algo...

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International Conference on Advances in Engineering (ICAE)

作者： Li, Chun Sheng Guang Dong Univ Business Studies Dept Math & Computat Sci Guangzhou 510320 Guangdong Peoples R China

This paper defines nearest neighbor pair and puts forward four assumptions about nearest neighbor pairs, based on which a center initialization method for k-means algorithm over data sets with two clusters is build. Experiments on real data sets show that the proposed method is not preferable but at least comparable to the ones in literatures. The contribution of the proposed method is to open up a new approach to devising center initialization method for k-means algorithm. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICAE2011.

关键词： cluster center initialization k-means algorithm nearest neighbor rule

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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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Efficient Customer Segmentation Using Silhouette Based k-means algorithm 4

Efficient Customer Segmentation Using Silhouette Based K-Mea...

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4th International Conference on Artificial Intelligence and Signal Processing

作者： Chilla, Pavan karthik Nahak, Sunil kumar Deepika, Mudem Veera Venkata Rohit, Balivada Sai Hemalatha, Tankala Dalai, Asish kumar KL Univ Dept Comp Sci Vaddeswaram Andhra Pradesh India NIST Univ Dept Comp Sci & Engn Berhampur Odisha India Vijaya Inst Technol Women Dept Elect & Commun Engn Vijayawada Andhra Pradesh India VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Customer segmentation is now essential for firms seeking to maintain and optimize revenue from consumers. The present research investigates the potential of utilizing behavioral variables such as expenses and income to successfully segment clients, beyond conventional demographic methods. The k-means clustering procedure, which is an unsupervised machine learning methodology, is utilized using the Elbow method and Silhouette score to ascertain the most suitable number of clusters. A Python application, with basic scaling, examines a dataset from a retail mall to visually represent similarities among customers and categorize them into clusters. The project aims to segment customers based on their purchase behavior using the silhouette-based k-means algorithm. The seaborn library will be used for implementation. The objective is to create a resilient consumer segmentation approach that assists organizations in comprehending target markets and enhancing marketing strategies suitably. By employing this algorithm, organizations acquire knowledge about client behavior, facilitating customized marketing campaigns and enhanced income generat- ing.

关键词： Customer segmentation clustering k-means algorithm silhouette analysis machine learning data analysis marketing strategy behavioral traits

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A Decreasing k-means algorithm for the Disk Covering Tour Problem in Wireless Sensor Networks 20

A Decreasing <i>k</i>-Means Algorithm for the Disk Covering ...

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20th IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yang, Jia-Jiun Jiang, Jehn-Ruey Lai, Yung-Liang Natl Cent Univ Jhongli Taiwan Taoyuan Innovat Inst Technol Jhongli Taiwan

ISBN: (纸本)9781479976157

This paper studies a Disk Covering Tour Problem (DCTP) for reducing the energy consumption of a mobile robot's movement to provide services for sensor nodes in a wireless sensor network (WSN). Given a set of locations of sensor nodes and a starting location of mobile robot, the DCTP is to find a minimum cost tour of a sequence of tour stops for the mobile robot to serve sensor nodes by keeping every sensor node within a specified distance of a tour stop. We propose an algorithm, called Decreasing k-means (Dk-means), to find an approximate solution to the DCTP. The idea is to select a minimum number of disks or circles of a fixed radius to cover all sensor nodes, and then to find a minimum cost tour passing all disk centers. The simulation results show the proposed algorithm outperforms the related CSP (Covering Salesman Problem) algorithm and the QiF algorithm.

关键词： disk covering tour problem disk covering problem covering salesman problem wireless sensor network k-means algorithm

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Integration of distributed biological data using modified k-means algorithm

Integration of distributed biological data using modified K-...

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11th Pacific-Asia Conference on knowledge Discovery and Data Mining

作者： Jeong, Jongil Ryu, Byunggul Shin, Dongil Shin, Dongkyoo Sejong Univ Dept Comp Sci & Engn 98 Kunja Dong Seoul 143747 South Korea

ISBN: (纸本)9783540770169

The goals of bioinformatics are the solving of biological questions and the active driving of the work of biologists by offering search and analysis methods for research data. The internet brings us distributed environments in which we can access the databases of various research groups. However, a very large quantity of data always causes trouble, creating crucial problems, such as problems with the search for and analysis of data in these distributed environments. Data clustering can be a solution when searching for data. However, this task is very tedious because its execution time is directly proportional to the volume of data. In this paper we propose a distributed clustering scenario and a modified k-means algorithm for the efficient clustering of biological data, and demonstrate the enhancement in performance that it brings.

关键词： distributed clustering k-means algorithm bioinformatics

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A new improved k-means algorithm with penalized term

A new improved K-means algorithm with penalized term

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IEEE International Conference on Granular Computing

作者： Ding, Zejin Yu, Jian Zhang, Yan-Qing Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA Beijing Jiaotong Univ Sch Comp Sci Beijing 100044 Peoples R China

ISBN: (纸本)9780769530321

k-means algorithm is a popular method in cluster analysis. After reviewing different k-means algorithms, we propose the new penalized k-means algorithm. Originally inspired by the Maximum Likelihood(ML) method, a prior probability distribution assumed by classic k-means algorithm about the clustering data set was discovered, and then. the new objective function for the penalized k-means algorithm was introduced. By minimizing this function with genetic algorithm, results show that this method is better than k-means algorithm in some perspectives.

关键词： k-means algorithm Objective function Probability distribution

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Enhancement of k-means algorithm using ACO as an Optimization Technique on High Dimensional Data

Enhancement of K-Means algorithm using ACO as an Optimizatio...

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International Conference on Electronics and Communication Systems (ICECS)

作者： Aparna, k. Nair, Mydhili k. BMS Inst Technol Dept MCA Bangalore Karnataka India MS Ramaiah Inst Technol Dept ISE Bangalore Karnataka India

ISBN: (纸本)9781479966387

Clustering is a distribution of data into groups of similar objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The concept of clustering applications is particularly in the context of information retrieval and in organizing web resources. The objective of clustering is to find out information and in the present day context, to locate most relevant resources. In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Though the k-means is one of the best clustering algorithms, the quality is based on the starting condition and it may converge to local minima. There is not much of work done by the researchers to improve the cluster quality after grouping. We have proposed a novel method to improve the cluster quality on high dimensional data set by ant based refinement algorithm. The Ant Colony Optimization algorithm (ACO) is one of the most widely used probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. An ant is a simple computational agent in the ant colony optimization algorithm. It develops an iterative solution for any problem at hand. The intermediate solutions can be used to arrive at the final solution. The proposed algorithm is tested using data from different domain and the results show that refined initial starting points and post processing refinement of clusters based on ACO can lead to improved solutions in terms of entropy, time taken and accuracy of clusters.

关键词： k-means algorithm Ant Colony Optimization (ACO) High Dimensional Data Entropy Accuracy

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