Description: 数据挖掘中，判别树算法的应用例子。属于分类中的算法 很好。-data mining, discriminant Tree Algorithm application examples. The classification is a good algorithm. Platform: |
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Author:cxl |
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Description: 程序用C语言实现了贝叶斯在数据挖掘中分类和预测中的应用，通过程序可以很好的进行分类和预测。-This program use C language to realize the application of bayes in data mining and forecasting,you can use this program to make a good classification and forecasting
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Author:龚文引 |
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Description: 数据挖掘在公司财务分析中的应用，用vc编程，希望能够帮助大家-data mining in financial analysis, the application vc programming, we hope to help Platform: |
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Author:小强强 |
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Description: 小叶波算法，用有数据压缩在数据挖掘和图像的压缩中有很好的应用，-small Ye Bo algorithm, data compression is used in data-mining and image compression is a very good application, Platform: |
Size: 117760 |
Author:mou |
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Description: 本书为数据挖掘。。介绍了数据挖掘的基本概念。基本方法，原理及其应用。-the book for data mining. . Data mining of the basic concept. The basic method, principle and its application. Platform: |
Size: 2369536 |
Author:cxl |
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Description: Form1.cs是应用聚类算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通过两个参数EPS和MinPts调节聚类。DBSCAN.cs是全部算法的实现文件，聚类算法的进一步信息请参考“数据挖掘”或者相关书籍。聚类示例数据来自于sxdb.mdb，一个Access数据库。-Form1.cs clustering algorithm is applied DBSCAN (Density-Based S patical Clustering of Application with Noise) example, the two parameters can EPS and MinPts regulation clustering. DBSCAN.cs algorithm is the realization of all documents, the clustering algorithm further information please refer to the "data mining" or books. Clustering sample data from sxdb.mdb, an Access database. Platform: |
Size: 26624 |
Author:yang |
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Description: 粗糙集应用软件，方便完成数据挖掘、知识总结-Rough Set application software to facilitate the completion of data mining, knowledge summary Platform: |
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Author:xmj |
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Description: 程序说明：
Form1.cs是应用聚类算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通过两个参数EPS和MinPts调节聚类。
DBSCAN.cs是实现文件，聚类算法的进一步信息请参考“数据挖掘”或者相关书籍
聚类示例数据来自于sxdb.mdb，一个Access数据库。
已知问题及进一步改进建议：
问题：dbscan.cs行64，SortedList不支持重复键，因此若两个数据点距离相同则无法加入集合
解决：采用人为减小一个微小量，使数据点距离不同且不影响聚类结果
上一解决方案的问题：减小double.Epsilon微小量无助于使SortedList认为两点距离以及不同
解决：采用一个指数增长的微小量，连续重试直至SortedList认为距离已经不同
进一步改进建议：可能通过double的强制转型为内存中的byte类型（假设double型转为8个byte）
然后最后一个byte减去0x01可比较漂亮的解决问题，但是……呵呵，C#中我不会这个操作
也可以自己实现一个SortedList，支持重复键，当然，这，好像是微软应该做的工作了 ^_^
Eric Guo
<http://www.cnblogs.com/ericguo/>
-procedures : Form1.cs clustering algorithm is applied DBSCAN (Density-Based Spati cal Clustering of Application with Noise) example, two parameters can EPS and MinPts regulation clustering. DBSCAN.cs is, the clustering algorithm further information please refer to the "data mining" or books related data clustering example from sxdb.m db, an Access database. Known issues and recommendations for further improvement : : 64 dbscan.cs OK, SortedList not support duplicate keys, and therefore if two data points from the same pool can not be solved by adding : By applying an artificially reduce a small amount of data from different points without clustering results on the impact of a solution of the problem : double.Epsilon small decrease in the amount of helplessness to make that 2:00 S Platform: |
Size: 26624 |
Author:Huang Yi |
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Description: 数据仓库技术和OLAP研究，数据仓库、数据挖掘和OLAP分析及其应用实例-data warehouse and OLAP technology research, data warehouse, data mining and OLAP Analysis and Application Examples Platform: |
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Author:陈建军 |
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Description: 这是一本粗糙集的数据挖掘经典书籍，书中不但介绍了粗糙集的相关知识，并给出了相关应用，同时介绍并比较了其他挖掘方法。-This is a rough set data mining classic books, the book not only introduced a rough set of relevant knowledge, and gives the related application, at the same time and compare the introduction of other mining methods. Platform: |
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Author:阿米 |
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Description: OpenI provides a web-driven interface to build and publish interactive reports from OLAP data sources.Going beyond that, OpenI aims to provide consolidated analysis from all the key data components of an intelligent application, including:
OLAP data sources
Relational databases
Statistical data models
Data mining models Platform: |
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Author:陈晨 |
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Description: 16C84做的智能卡程序,滚动码应用原程序,16c54四位LED时钟显示程序,红外应用源程序集1,用PIC12C508509 控制一个步进电机源程序,模拟数据采器 USART-16C84 make smart card procedures, the application of original procedures for rolling code, 16c54 four LED clock display program, one set of infrared source applications, using a stepper motor control PIC12C508509 source, analog data mining USART device Platform: |
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Author:wwdragonfly |
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Description: 基于聚类的数据挖掘技术在CRM中的研究与应用-Cluster-based data mining technology in the CRM Research and Application Platform: |
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Description: 数据挖掘经过十几年的研究，很多传统问题获得了大量研究．近年来，数据挖掘研究与应用迅猛发展，出现了很多新的方法、系统和应用．首先介绍了数据挖掘概念、起源，然后总结了数据挖掘技术的最新发展，便于数据挖掘研究者进行资料的整理和总结．-Data Mining, after ten years of research, many traditional problems gained a great deal of study. In recent years, data mining research and application of rapid development, there were a lot of new methods, systems and applications. First introduced the concept of data mining, the origin, and then summed up the latest data mining technology development, to facilitate data mining researchers to collate and summarize information. Platform: |
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Description: 这个程序主要讲述了数据挖掘中K均值算法的应用-This process focuses on data mining in the application of K-means algorithm Platform: |
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Author:郭乌行 |
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Description: Form1.cs是应用聚类算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通过两个参数EPS和MinPts调节聚类。
DBSCAN.cs是实现文件，聚类算法的进一步信息请参考“数据挖掘”或者相关书籍
聚类示例数据来自于sxdb.mdb，一个Access数据库-Form1.cs is the application of clustering algorithm DBSCAN (Density-Based Spatical Clustering of Application with Noise) of the sample, two parameters can MinPts regulation EPS and clustering. Is to achieve DBSCAN.cs document clustering algorithm further information please refer to " data mining" or books related to clustering of data from sample sxdb.mdb, an Access database Platform: |
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Description: The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.-The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Platform: |
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Author:Tthias |
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Search - data mining application