Description: 数据挖掘领域中的一种算法－ML-KNN是一种改进的最近邻算法-Data Mining in the area of an algorithm-ML-KNN is an improved nearest neighbor algorithm Platform: |
Size: 5120 |
Author:李平 |
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Description: knn和Native Bayes算法实现,两个实现在一起,是数据挖掘和机器学习中的内容.-KNN and Native Bayes algorithm, the two to achieve together, is data mining and machine learning content. Platform: |
Size: 174080 |
Author: |
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Description: 数据挖掘导论中的K近邻聚类算法，用C++编写而成。-Introduction to Data Mining of the K neighbors clustering algorithm, using C++ has been prepared by. Platform: |
Size: 440320 |
Author:绍敏 |
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Description: 对数据挖掘中KNN算法的代码实现。包含实验报告格式、可实现代码及代码解析。-KNN algorithm for data mining in the code implementation. Contains the experiment report format, enabling code and code analysis. Platform: |
Size: 22528 |
Author:xb |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification, Platform: |
Size: 622592 |
Author:sukmawati |
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Description: This paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM,
Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification Platform: |
Size: 635904 |
Author:ParisM |
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Description: 寻找测试样本的最近邻，可以有效的用于用于模式识别，信号处理-This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.
The code was initially implemented through vectorization. After discussions with John D Errico, I realized that my algorithm will suffer numerical accurancy problem for data with large values. Then, after trying several approaches, I found simple loops with JIT acceleration is the most efficient solution. Now, the performance of the code is comparable with kd-tree even the latter is coded in a mex file.
The code is very simple, hence is also suitable for beginner to learn knn search. Platform: |
Size: 3072 |
Author:刘晓红 |
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Description: （经典聚类算法）
国际权威的学术组织the IEEE International Conference on Data Mining (ICDM) 2006年12月评选出了数据挖掘领域的十大经典算法：C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
不仅仅是选中的十大算法，其实参加评选的18种算法，实际上随便拿出一种来都可以称得上是经典算法，它们在数据挖掘领域都产生了极为深远的影响。-(Classical clustering algorithm)
International authoritative academic organization of the IEEE International Conference on Data Mining (ICDM) in December 2006 selected the top ten of the field of data mining algorithm: the C4.5, k-Means, SVM, of Apriori, the EM, the PageRank, AdaBoost, kNN , the Naive Bayes, and the CART.
Not just the selected 10 algorithms, in fact, participate in the selection of 18 kinds of algorithms, in fact, easily come up with one can be called a classical algorithm in the field of data mining, they have had far-reaching impact. Platform: |
Size: 3922944 |
Author:赵鑫维 |
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Description: 介绍数据挖掘的10种主要算法及其应用 一种透过数理模式来分析企业内储存的大量资料，以找出不同的客户或市场划分，分析出消费者喜好和行为的方法。 -Top 10 algorithms in data mining his paper presents the top 10 data mining algorithms identified by the IEEE
International Conference on Data Mining (ICDM) in December 2006: C4.5,k-Means, SVM,
Apriori, EM, PageRank, AdaBoost,kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community. With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
review current and further research on the algorithm. These 10 algorithms cover classification, Platform: |
Size: 633856 |
Author:andyzygg |
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Description: 这是一个小而有效的程序来执行的K -近邻搜索算法，此算法利用JIT 理论加速循环，比向量化有效解决了大量数据的精度问题。甚至比kd-tree效果要佳。
K-nearest neighbor search已经广泛应用在科学与工程上，比如模式识别，数据挖掘和信号处理。
-This is a small and effective procedures to implement the K- nearest neighbor search algorithm, this algorithm JIT theoretical acceleration cycle, than to quantify an effective solution to the large amounts of data accuracy problems. Even more than the effect of kd-tree to be good. K-nearest neighbor search has been widely used in science and engineering, such as pattern recognition, data mining and signal processing. Platform: |
Size: 3072 |
Author:hxl |
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Description: An Improved KNN Text Classification Algorithm
Based on Clustering
With the rapid development of internet, a large number
of text information begin to exist with the form of
computer-readable and increase exponentially. The data
and resource of internet take on the character of massive.
In order to effectively manage and utilize this large
amount of document data, text mining and content-based
information retrieval have gradually become the hotspot
research field in the world. Platform: |
Size: 180224 |
Author:AMIMIMEK |
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Description: 邻近算法，或者说K最近邻(kNN，k-NearestNeighbor)分类算法是数据挖掘分类技术中最简单的方法之一。所谓K最近邻，就是k个最近的邻居的意思，说的是每个样本都可以用它最接近的k个邻居来代表。
kNN算法的核心思想是如果一个样本在特征空间中的k个最相邻的样本中的大多数属于某一个类别，则该样本也属于这个类别，并具有这个类别上样本的特性。该方法在确定分类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 kNN方法在类别决策时，只与极少量的相邻样本有关。由于kNN方法主要靠周围有限的邻近的样本，而不是靠判别类域的方法来确定所属类别的，因此对于类域的交叉或重叠较多的待分样本集来说，kNN方法较其他方法更为适合。-Nearby algorithm, or K-nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of classification data mining technology in the most simple way. The so-called K-nearest neighbor is the k nearest neighbors meant to say is that it can be used for each sample k nearest neighbors to represent. kNN algorithm core idea is that if a sample in feature space is k-nearest neighbor samples most belong to a category, the sample also fall into this category, and the category having the characteristics of the sample. The method in determining the classification decision based solely on the nearest one or several samples to determine the category to be sub-sample belongs to the category. kNN method when category decisions, with only a very small amount of adjacent samples related. Because kNN method is mainly limited by the surrounding adjacent samples, rather than the domain identification method to determine the class belongs to the category, so for class field of overlap or more s Platform: |
Size: 2048 |
Author:黑色地位 |
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Description: 该程序是用python编写一个K近邻算法，通过该例子可以掌握K近邻算法，是学习数据挖掘的一个高效的算法。-The program is written in python a K-nearest neighbor algorithm, this example can grasp the K-nearest neighbor algorithm, a learning data mining and efficient algorithms. Platform: |
Size: 1024 |
Author:liuchao |
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Description: 邻近算法，或者说K最近邻(kNN，k-NearestNeighbor)分类算法是数据挖掘分类技术中最简单的方法之一。所谓K最近邻，就是k个最近的邻居的意思，说的是每个样本都可以用它最接近的k个邻居来代表。(Neighborhood algorithm, or K nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of the simplest methods in data mining classification technology. The so-called K nearest neighbor, that is, k the nearest neighbor's meaning, that is, each sample can use it to the nearest k neighbors to represent.) Platform: |
Size: 1024 |
Author:折夏
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Description: 邻近算法，或者说K最近邻(kNN，k-NearestNeighbor)分类算法是数据挖掘分类技术中最简单的方法之一(The adjacent algorithm, or the K nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of the simplest methods in the data mining classification technique) Platform: |
Size: 1024 |
Author:壶中月色凉
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