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Search Results: 1 - 10 of 314816 matches for " incremental candidate<br>聚类 "
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Efficient K-medoids clustering algorithm
一种高效的K-medoids聚类算法

夏宁霞,苏一丹,覃希
计算机应用研究 , 2010,
Abstract: Due to the disadvantages of sensitivity to the initial selection of the medoids and poor performance in large data set processing in the K-medoids clustering algorithm, this paper proposed an improved K-medoids algorithm based on a fine-tuned of initial medoids and an incremental candidate set of medoids. The proposed algorithm optimized initial medoids by fine-tu-ning and reduced computational complexity of medoids substitution through expanding medoids candidate set gradually. Expenrimental results demonstrate the effectiveness of this algorithm,which can improve clustering quality and significantly shorten the time in calculation compared with the traditional K-medoids algorithm.
IC-kmedoids:适用于RNA二级结构预测的聚类算法
IC-kmedoids: A Clustering Algorithm for RNA Secondary Structure Prediction

王常武,刘小凤,王宝文,刘文远
- , 2015, DOI: 10.7507/1001-5515.20150018
Abstract: 采用自由能方法预测RNA二级结构时, 如何精确有效地从次优结构中筛选出真实的二级结构成为RNA结构预测中的关键。采用聚类技术对次优结构集合进行分析, 可有效地提高预测结果的精度。本文利用RBP分数矩阵, 提出一种基于增量中心候选集的改进k-medoids算法。它将随机选择初始中心并进行首次划分后以中心候选集逐一扩展的方式进行中心轮换, 以降低算法的复杂度。实验表明, 该算法能取得更高的CH值, 且能有效地缩短计算时间。
Due to the minimum free energy model, it is very important to predict the RNA secondary structure accurately and efficiently from the suboptimal foldings. Using clustering techniques in analyzing the suboptimal structures could effectively improve the prediction accuracy. An improved k-medoids cluster method is proposed to make this a better accuracy with the RBP score and the incremental candidate set of medoids matrix in this paper. The algorithm optimizes initial medoids through an expanding medoids candidate sets gradually.The predicted results indicated this algorithm could get a higher value of CH and significantly shorten the time for calculating clustering RNA folding structures.
An Incremental Grid Density-Based Clustering Algorithm
基于密度的增量式网格聚类算法

CHEN Ning,ZHOU Long-xiang,CHEN An,
陈宁
,陈安,周龙骧

软件学报 , 2002,
Abstract: Although many clustering algorithms have been proposed so far, seldom was focused on high-dimensional and incremental databases. This paper introduces a grid density-based clustering algorithm GDCA. which discovers clusters with arbitrary shape in spatial databases. It first partitions the data space into a number of units, and then deals with units instead of points. Only those units with the density no less than a given minimum density threshold are useful in extending clusters. An incremental clustering algorithm----IGDCA is also presented, applicable in periodically incremental environment.
Incremental clustering algorithm based on grid
一种基于网格的增量聚类算法

YIN Gui-sheng,YU Xiang,NING Hui,
印桂生
,于翔,宁慧

计算机应用研究 , 2009,
Abstract: This paper analyzed the existing clustering algorithms based on grid,and the clustering algorithms based on grid had the advantages of dealing with high dimensional data and high efficiency. However, traditional algorithms based on grid were influenced greatly by the granularity of grid partition.It proposed an incremental clustering algorithm based on grid, which was called IGrid.IGrid had the advantage of high efficiency of traditional clustering algorithms based on grid, and it also partitioned the grid space by dimensional radius in a dynamic and incremental manner to improve the quality of clustering.The experiments on real datasets and synthetic datasets show that IGrid has better performance than traditional clustering algorithms based on grid in both speed and accuracy.
Peer Clustering Based on Incremental Learning in P2P Networks
P2P网络中基于增量学习的节点聚类

ZHANG Hua-Xiang,LIU Pei-De,HUANG Shang-Teng,
张化祥
,刘培德,黄上腾

计算机科学 , 2005,
Abstract: This paper discusses the content-based peer clustering in peer-to-peer networks.Information retrieval based on accurate match of keywords in filenames ignores the document semantics and the similarity between documents.If peers are clustered according to the similarity between their released documents of a special interest topic,and the in- formation query is executed among peers of a specific cluster,the efficiency should be improved.We propose an incre- mental learning approach to peer clustering,and employ an interest crawler agent to calculate a peer's score.Whether a peer joins in a cluster or not is determined by its score.Experimental results demonstrate that clustering of peers in hybrid p2p networks is both accurate and more efficient for irformation retrieval.
Incremental clustering algorithm based on representative points
一种基于代表点的增量聚类算法

MENG Fan-rong,LI Xiao-cui,ZHOU Yong,
孟凡荣
,李晓翠,周 勇

计算机应用研究 , 2012,
Abstract: As the existing incremental clustering algorithms have various disadvantages such as high sensitivity to parameters, high time-space complexity, etc. This paper presented an incremental algorithm based on representative points. It first used the static clustering algorithm based on representative points to cluster the original data set. Then according the relationship between the new points and the existing representative points, the algorithm judged whether the new points should be added to the clusters containing the existing representative points or promoted as new representative points. Finally it used the static clustering algorithm again to cluster the new points. Experimental result shows that this algorithm is insensible to parameters, efficient and occupies little space.
Incremental and Distributed Web Page Clustering Algorithms PG+ and PG++
渐进/分布式网页聚类算法PG+与PG++

WANG Qi-xin,LI Yi,DONG Li,NIE Yu,WANG Ke-hong,
王启新
,李毅,董丽,聂宇,王克宏

软件学报 , 2002,
Abstract: A user behavior analysis is an important approach in many algorithms for the Web site information recommendation, among which, PageGather is a typical algorithm. However, the original PageGather algorithm is static, which needs too many data inputs and too much computing time. In this paper, incremental learning and distributed computation mechanisms are introduced into PageGather, so that two improved algorithms PG+ and PG++ are proposed. At the same time, corresponding experimental results are presented and analyzed.The improved algorithms are equivalent to the static PageGather algorithms.And better effect has got.
Incremental algorithm for clustering texts in internet-oriented topic detection
一种面向网络话题发现的增量文本聚类算法*

YIN Feng-jing,XIAO Wei-dong,GE Bin,LI Fang-fang,
殷风景
,肖卫东,葛斌,李芳芳

计算机应用研究 , 2011,
Abstract: 为满足网络舆情监控系统中话题发现的需要,并克服经典single-pass算法处理网络文本聚类中受输入顺序影响和精度较低的主要不足,提出了ICIT算法,继承了single-pass算法的简单原理,保证了网络文本聚类的实时性;通过正文分词时标注词性选择名词动词进行正文向量化、建立文本标题向量来与文本正文向量共同表征文本、采用average-link策略、引入“代”的概念分批进行文本的聚类,以及在每批次聚类后添加报道重新选择调整所属的步骤来提高聚类的质量。实验证明了ICIT算法在提高话题发现准确度上的有效性和实用性。
Survey of Polygonal Model Simplification Algorithms
多边形模型简化算法综述

GUO Li-zhen,WU En-hu,
郭力真
,吴恩华

计算机应用研究 , 2005,
Abstract: Gives a survey of polygonal model simplification algorithms.The algorithms are classified into 4 basic polygon removal mechanisms, and introduction is given to each class on its basic principle, strength, weakness, and various algorithms published on the class. Finally, the paper provides some comments on how to select proper simplification method in different applications.
增量式神经网络聚类算法
刘培磊, 唐晋韬, 谢松县, 王挺
LIU Peilei
, TANG Jintao, XIE Songxian, WANG Ting

- , 2016, DOI: 10.11887/j.cn.201605021
Abstract: 神经网络模型具有强大的问题建模能力,但是传统的反向传播算法只能进行批量监督学习,并且训练开销很大。针对传统算法的不足,提出全新的增量式神经网络模型及其聚类算法。该模型基于生物神经学实验证据,引入新的神经元激励函数和突触调节函数,赋予模型以坚实的统计理论基础。在此基础上,提出一种自适应的增量式神经网络聚类算法。算法中引入“胜者得全”式竞争等学习机制,在增量聚类过程中成功避免了“遗忘灾难”问题。在经典数据集上的实验结果表明:该聚类算法与K-means等传统聚类算法效果相当,特别是在增量学习任务的时空开销方面具有较大优势。
Neural network model is powerful in problem modelling. But the traditional back propagating algorithm can only execute batch supervised learning, and its time expense is very high. According to these problems, a novel incremental neural network model and the corresponding clustering algorithm were put forward. This model was supported by biological evidences, and it was built on the foundation of novel neuron’s activation function and the synapse adjusting function. Based on this, an adaptive incremental clustering algorithm was put forward, in which mechanisms such as “winner-take-all” were introduced. As a result, “catastrophic forgetting” problem was successfully solved in the incremental clustering process. Experiment results on classic datasets show that this algorithm’s performance is comparable with traditional clustering models such as K-means. Especially, its time and space expenses on incremental tasks are much lower than traditional clustering models.
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