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本代码是关于数据挖掘的层次聚类算法的JAVA编程实例,请勿用于商业用途上-The code is JAVA programming examples on data mining hierarchical clustering algorithm, not for commercial use
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PDF格式的PPT,来自英国南安普顿大学。主要介绍了数据挖掘的技术以及应用,包括决策树,推荐系统,文本聚类,搜索引擎,购物篮子分析。-PPT PDF format, the University of Southampton. It introduces data mining technology and applications, including decision, recommendation systems, text clustering, search engines, sho
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数据挖掘中DBSCAN聚类算法的实现,用python语言实现,亲测可用。-The realization of DBSCAN clustering algorithm in data mining, using Python language to achieve, pro test available.
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Algorithm k-means is a simple iterative clustering algorithm, which divides the set of data to a user-specified number of clusters, k. The algorithm is simple to implement and run relatively fast, easily adaptable and common in practice. It is his
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数据挖据中聚类问题的实现 通过简单数据进行聚类分析 该聚类为最大最小聚类-According to the data mining, the clustering problem is achieved by using simple data to cluster analysis.
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数据挖掘 (DBSCAN)密度聚类 ,包括聚类数据,文档描述,源代码-(DBSCAN) density clustering data mining, including data clustering, a document descr iption, the source code
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Davies Bouldin index戴维森堡丁指数做数据挖掘的时候,经常你不知道要做多少个Cluster,那么你就需要先按照大体构想来分那么几种测试性聚类,通过计算这个指数,来确定到底哪个Cluster最合理。当然,算法并没有十全十美的,你只能说是理论上合理了,实际这个指数能给别人多大帮助我自己真的也不得而知。-Davies Bouldin index 戴维森堡丁 index for data mining, often you do not know how many Cluster, t
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这是数据挖掘中的k均值聚类算法,用java语言编写的,对于搞聚类的人士很有帮助-This is the data mining k-means clustering algorithm, using java language, for persons engaged in clustering helpful
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聚类分析是数据挖掘研究领域中一个非常活跃的研究课题) 本文重点分析了高维度数据的自动子空间聚类算法-Cluster analysis is a data mining research area in a very active research topic) This paper focuses on automatic subspace clustering algorithm for high dimensional data
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数据挖掘算法,用于聚类分析,代码显示的是Kmeans算法,特点是,可以将结果显示在EXCEL表中,显示的结果顺序和输入的数据顺序一致-Data mining algorithm for clustering analysis, the code shows the Kmeans algorithm is characterized by the results can be displayed in the EXCEL table, the results show that the order
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自适应临近聚类算法/集群和投影聚类/自适应的邻居 -Clustering and Projected Clustering with Adaptive Neighbors.
The 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York, USA, 2014
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机器学习和数据挖掘中常用的K-means聚类算法,包含两个文件,kmeans.py是Python实现代码,bank-data.csv是测试数据-Machine learning and data mining commonly used K-means clustering algorithm contains two files, kmeans.py is a Python implementation code, bank-data.csv test data
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模糊聚类算法源码,通过迭代聚类中心,以及隶属度函数,完成代码运算,用于数据挖掘初学者使用。(Fuzzy clustering algorithm source code, through the iterative clustering centers and membership function, complete code for data mining operations, for beginners to use.)
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一个简单的k均值聚类例程,适合数据挖掘初学者练习(A simple K mean clustering routines, practice for data mining beginners.)
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k-mediod、knn、uci数据集。
数据挖掘、机器学习中的经典聚类、分类算法(K-mediod, KNN, and UCI data sets.
Data mining and classical clustering and classification algorithms in machine learning)
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学习数据挖掘基础,以及人工智能的基础知识,主要是聚类(The basic knowledge of learning data mining and artificial intelligence is mainly clustering)
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贝叶斯分类,主要是数据挖掘以及人工智能的相关知识,基础知识(The basic knowledge of learning data mining and artificial intelligence is mainly clustering)
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K-means聚类算法的matlab实现(k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each obse
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数据挖掘算法R语言实现,包括聚类、判别、集成学习、随机森林、神经网络、支持向量机等算法。(Data mining algorithm R language implementation, including clustering, discrimination, ensemble learning, random forest, neural network, support vector machines and other algorithms.)
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聚类分析是数据挖掘中重要的研究内容之一,对聚类准则进行了总结,对五类传统的聚类算法的研究 现状和进展进行了较为全面的总结,就一些新的聚类算法进行了梳理,根据样本归属关系、样本数据预处理、 样本的相似性度量、样本的更新策略、样本的高维性和与其他学科的融合等六个方面对聚类中近 20多个新算 法,如粒度聚类、不确定聚类、量子聚类、核聚类、谱聚类、聚类集成、概念聚类、球壳聚类、仿射聚类、数据流聚 类等,分别进行了详细的概括。(Clustering analysis is one of the impor
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