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[Industry research] clustering_survey DL : 0: a documentation describes clustering data mining, Survey of Clustering Data Mining Techniques from Pavel Berkhin Accrue Software, Inc.
Date : 2025-12-20 Size : 556kb User : mabu
[Industry research] High DL : 0: This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best fit the data. We obtain a robust clustering method called High- Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images in a probabilistic framework. Experiments on a recently proposed database demonstrate the effectiveness of our clustering method for category localization.
Date : 2025-12-20 Size : 189kb User : tra ba huy
[Industry research] p103-zhang DL : 0: Research about Birch data clustering algorithm
Date : 2025-12-20 Size : 1.35mb User : amila banuka
[Industry research] An_Improveme_of_LEACH_Routing_Protocol_Based_on_T DL : 0: Abstract—LEACH is a hierarchy routing protocol for WSN (wireless sensor networks), which is superior to direct communication protocol, minimum-transmission-energy protocol and static clustering protocol. owever, LEACH itself has some defects. In this paper LEACH-TM introduces the concept of Trust, designs the cluster-head adjusting procedure and establishes multi-path with cluster-heads ting as routers.The simulation illustrates that LEACH-TM makes much progress in the reliability of data transmission, the tribution of cluster heads and the lifetime of networks.-Abstract—LEACH is a hierarchy routing protocol for WSN (wireless sensor networks), which is superior to direct communication protocol, minimum-transmission-energy protocol and static clustering protocol. owever, LEACH itself has some defects. In this paper LEACH-TM introduces the concept of Trust, designs the cluster-head adjusting procedure and establishes multi-path with cluster-heads ting as routers.The simulation illustrates that LEACH-TM makes much progress in the reliability of data transmission, the tribution of cluster heads and the lifetime of networks.
Date : 2025-12-20 Size : 260kb User : Amir
[Industry research] ESCAL_An_Energy_Saving_Clustering_Algorithm_Based DL : 0: Abstract—the application of wireless sensor network (WSN) is always restricted by the energy shortage of sensor nodes. In order to reduce the entire energy consumption of the WSN, a promising approach is to design light clustering algorithms.LEACH is such a well-known clustering algorithm that was designed to distribute the energy consumption to nodes in the WSN evenly. LEACH is characterized by its attactive clusterbased strategy however, in the algorithm, energy dissipation of entire network is still great during the process that cluster-heads (CH) transmit data to base station (BS). In this paper, based on LEACH, we propose a modified clustering orithm which is named ESCAL. In ESCAL, CHs won’t communicate with BS directly, but transfer aggregated data to the nearest node in term of received signal strength then this elected nearest node willcompress and forward the data to BS.-Abstract—the application of wireless sensor network (WSN) is always restricted by the energy shortage of sensor nodes. In order to reduce the entire energy consumption of the WSN, a promising approach is to design light clustering algorithms.LEACH is such a well-known clustering algorithm that was designed to distribute the energy consumption to nodes in the WSN evenly. LEACH is characterized by its attactive clusterbased strategy however, in the algorithm, energy dissipation of entire network is still great during the process that cluster-heads (CH) transmit data to base station (BS). In this paper, based on LEACH, we propose a modified clustering orithm which is named ESCAL. In ESCAL, CHs won’t communicate with BS directly, but transfer aggregated data to the nearest node in term of received signal strength then this elected nearest node willcompress and forward the data to BS.
Date : 2025-12-20 Size : 84kb User : Amir
[Industry research] Data-clustering-particle-swarm-optimization DL : 0: Data clustering using particle swarm optimization Data clustering using particle swarm optimization
Date : 2025-12-20 Size : 309kb User : a2maridz
[Industry research] Kernel-Function-Clustering-Based-on-Ant-Colony-Al DL : 0: Cluster analysis one of the several important tools in modern data analysis, and the clustering can be regarded as an optimization problem. The underlying assumption is that there are natural tendencies of cluster or group structure in the data and the goal is to be able to uncover this structure. In general, traditional clustering algorithms are suitable to implement clustering only if the feature differences of data are large.-Cluster analysis is one of the several important tools in modern data analysis, and the clustering can be regarded as an optimization problem. The underlying assumption is that there are natural tendencies of cluster or group structure in the data and the goal is to be able to uncover this structure. In general, traditional clustering algorithms are suitable to implement clustering only if the feature differences of data are large.
Date : 2025-12-20 Size : 242kb User : rishi
[Industry research] An-Improved-Ant-Colony-Clustering-Algorithm-Based DL : 1: Focused on the disadvantage of classical Euclidian distance in data clustering analysis, we propose an improved distance calculation formula, which describes the local compactness and global connectivity between data points. Furthermore, we improve ant-colony clustering algorithm by using the improved distance calculation formula. Theoretical analysis and experiments show that this method is more efficient and has the ability to identify complex nonconvex clusters.
Date : 2025-12-20 Size : 345kb User : rishi
[Industry research] Studies-on-Fuzzy-C-Means-Based-on-Ant-Colony-Algo DL : 0: A fault identification with fuzzy C-Mean clustering algorithm based on improved ant colony algorithm (ACA) is presented to avoid local optimization in iterative process of fuzzy C-Mean (FCM) clustering algorithm and the difficulty in fault classification. In the algorithm, the problem of fault identification is translated to a constrained optimized clustering problem. Using heuristic search of colony can find good solutions. And according to the information content of cluster center, it could merger surrounding data together to cause clustering identification. The algorithm may identify fuzzy clustering numbers and initial clustering center. It can also prevent data classification from causing some errors. Thus, applying in fault diagnosis shows validity of computing and credibility of identification results.
Date : 2025-12-20 Size : 267kb User : rishi
[Industry research] OPTICS-algorithm---Wikipedia--the-free-encycloped DL : 0: OPTICS ("Ordering Points To Identify the Clustering Structure") is an algorithm for finding density-based clusters in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jö rg Sander[1]. Its basic idea is similar to DBSCAN,[2] but it addresses one of DBSCAN s major weaknesses: the problem of detecting meaningful clusters in data of varying density. In order to do so, the points of the database are (linearly) ordered such that points which are spatially closest become neighbors in the ordering. Additionally, a special distance is stored for each point that represents the density that needs to be accepted for a cluster in order to have both points belong to the same cluster. This is represented as a dendrogram.
Date : 2025-12-20 Size : 169kb User : swap
[Industry research] DBSCAN---Wikipedia--the-free-encyclopedia DL : 0: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jö rg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.[2] OPTICS can be seen as a generalization of DBSCAN to multiple ranges, effectively replacing the parameter with a maximum search radius.
Date : 2025-12-20 Size : 136kb User : swap
[Industry research] ainet DL : 0: This paper explores basic aspects of the immune system and proposes a novel immune network model with the main goals of clustering and filtering redundant data from problems described by a set of discrete samples. It is not our concern to reproduce with confidence any immune phenomenon, but to show that immune concepts can be used to develop novel computational tools for data processing. As important results of our model, the network evolved will be capable of reducing redundancy, describing data structure, shapes and their cluster inter-relations. The data clustering approach will be will be implemented in association with a statistical technique, and the network performance will be illustrated using two benchmark problems. The paper is concluded with a trade-off between the proposed network and artificial neural networks.
Date : 2025-12-20 Size : 89kb User : namareq
[Industry research] a_framework_ DL : 0: 数据挖掘和数据处理的注意点和方法，专业术语熵，聚类等的算法说明-Algorithm description of the data mining and data processing attention points and methods, terminology, entropy, clustering, etc.
Date : 2025-12-20 Size : 166kb User : 工艺u
[Industry research] 3Vol27No1 DL : 0: A COMPARATIVE ANALYSIS BETWEEN K-MEDOIDS AND FUZZY C-MEANS CLUSTERING ALGORITHMS FOR STATISTICALLY DISTRIBUTED DATA POINTS
Date : 2025-12-20 Size : 290kb User : mardak
[Industry research] 10.1.1.64.263 DL : 0: Data Clustering Algorithm Based on Gravity Theory
Date : 2025-12-20 Size : 206kb User : Yugal Kumar
[Industry research] soria2008a DL : 0: Breast Cancer Data Clustering using various segmentation algorithms.
Date : 2025-12-20 Size : 173kb User : PRASHANTH
[Industry research] ShihHsiang DL : 0: 数据拟合与分群方法于强健语音特征提取之研究-Exploring the Use of Data Fitting and Clustering Techniques for Robust Speech Recognition
Date : 2025-12-20 Size : 732kb User : ll
[Industry research] DBSCAN DL : 0: The method of spatial data clustering (using DBSCAN) and applications in the locate optimal ATM (Viet Nam Lang)
Date : 2025-12-20 Size : 112kb User : Duc Thang Vo
[Industry research] Subspace-clustering DL : 0: 过去的几年见证了一个explo比如来源和形式。例如,数以百万计的摄像机被安装在建筑物、街道、机场、城市和世界各地。这造成了巨大的进步如何获取、压缩、存储、传输和处理大量复杂的高维数据。-he past few years have witnessed an explo- ple sources and modalities. For example,millions of cameras have been installed in buildings, streets, airports, and cities around the world. This has generated extraordinary advances on how to acquire, compress, store, transmit, and process massive amounts of complex high-dimensional data.
Date : 2025-12-20 Size : 2.36mb User : 叶新
[Industry research] lecture-03-clustering-done DL : 0: Data mining clustering massive data into specified group
Date : 2025-12-20 Size : 87kb User : reidsneo

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