Description: 应用PCA进行特征提取和降维，可以应用于数据挖掘，机器学习，人脸识别上！-Application of PCA for feature extraction and dimensionality reduction can be applied to data mining, machine learning, face recognition on! Platform: |
Size: 1024 |
Author:choushi |
Hits:
Description: 数据挖掘技术在零售业中的应用，数据挖掘技术在零售业中的使用情况，个人感觉挺好的，值得下载下来-Data mining technology in the retail application, data mining technology in the retail industry in the use of the personal feeling quite good and worth a good look download Platform: |
Size: 12288 |
Author:wendell |
Hits:
Description: 改进关联规则算法在贴片机数据挖掘中的应用-Application of Improved Algorithm of Association Rules in Surface Mounter Data Mining Platform: |
Size: 18432 |
Author:shang |
Hits:
Description: 给出了linux集群系统的设计与实现，对并行程序设计中的消息传递机制给出了详细的说明，针对不同并行计算模型，研究了了并行程序设计的方法，结合数据挖掘技术等，给出并行计算平台的构建方法，并且对并行计算技术在化工过程与优化中的应用有很多优秀的观点。资料内容很丰富，对于研究并行技术的朋友有很大帮助。-Given Linux cluster system design and implementation of parallel programming, the message transfer mechanism gives detailed instructions, according to different parallel computing model, and the research indications parallel programming method and combining the data mining technology, etc, are parallel computing platform building method, and to parallel computing technology in the application of chemical process and optimization of a lot of good point of view. Material content is very rich, for studying parallel technology friend helps a lot.
Platform: |
Size: 25996288 |
Author:王鹏 |
Hits:
Description: 数据挖掘的应用，对数据集建立决策树，然后利用测试集中的数据判断测试。并给出最终的决策判断的正确率。-The application of data mining, decision tree on the data set, and then use the test to determine the test data set. The final decision making given the correct rate. Platform: |
Size: 7168 |
Author: |
Hits:
Description: past when the sample data was collected, the future predictions can
also be expected to be right.
Application of machine learning methods to large databases is called
data mining. The analogy is that a large volume of earth and raw material
is extracted from a mine, which when processed leads to a small
amount of very precious material similarly in data mining, a large volume
of data is processed to construct a simple model with valuable use,
for example-past when the sample data was collected, the future predictions can
also be expected to be right.
Application of machine learning methods to large databases is called
data mining. The analogy is that a large volume of earth and raw material
is extracted from a mine, which when processed leads to a small
amount of very precious material similarly in data mining, a large volume
of data is processed to construct a simple model with valuable use,
for example Platform: |
Size: 1024 |
Author:nikhilhhh |
Hits:
Description: past when the sample data was collected, the future predictions can
also be expected to be right.
Application of machine learning methods to large databases is called
data mining. The analogy is that a large volume of earth and raw material
is extracted from a mine, which when processed leads to a small
amount of very precious material similarly in data mining, a large volume
of data is processed to construct a simple model with valuable use,
for example-past when the sample data was collected, the future predictions can
also be expected to be right.
Application of machine learning methods to large databases is called
data mining. The analogy is that a large volume of earth and raw material
is extracted from a mine, which when processed leads to a small
amount of very precious material similarly in data mining, a large volume
of data is processed to construct a simple model with valuable use,
for example Platform: |
Size: 1024 |
Author:nikhilhhh |
Hits:
Description: past when the sample data was collected, the future predictions can
also be expected to be right.
Application of machine learning methods to large databases is called
data mining. The analogy is that a large volume of earth and raw material
is extracted from a mine, which when processed leads to a small
amount of very precious material similarly in data mining, a large volume
of data is processed to construct a simple model with valuable use,
for example-past when the sample data was collected, the future predictions can
also be expected to be right.
Application of machine learning methods to large databases is called
data mining. The analogy is that a large volume of earth and raw material
is extracted from a mine, which when processed leads to a small
amount of very precious material similarly in data mining, a large volume
of data is processed to construct a simple model with valuable use,
for example Platform: |
Size: 4096 |
Author:nikhilhhh |
Hits:
Description: 主要应用领域：
• 垂直搜索（Vertical Search）：也称为专业搜索，高速、海量和精确抓取是定题网络爬虫DataScraper的强项，每天24小时每周7天无人值守自主调度的周期性批量采集，加上断点续传和软件看门狗(Watch Dog)，确保您高枕无忧
• 移动互联网：手机搜索、手机混搭(mashup)、移动社交网络、移动电子商务都离不开结构化的数据内容，DataScraper实时高效地 采集内容，输出富含语义元数据的XML格式的抓取结果文件，确保自动化的数据集成和加工，跨越小尺寸屏幕展现和高精准信息检索的障碍。手机互联网不是 Web的子集而是全部，由MetaSeeker架设桥梁
• 企业竞争情报采集/数据挖掘：俗称商业智能（Business Intelligence），噪音信息滤除、结构化转换，确保数据的准确性和时效性，独有的广域分布式架构，赋予DataScraper无与伦比的情报采 集渗透能力，AJAX/Javascript动态页面、服务器动态网页、静态页面、各种鉴权认证机制，一视同仁。在微博网站数据采集和舆情监测领域远远领 先其它产品。-The main application areas:
• Vertical Search (Vertical Search): also known as professional search, speed, mass and precision is the SDI Web crawler to crawl the strengths DataScraper 24 hours a day 7 days a week periodic unattended batch capture self-scheduling, Canada and software watchdog on the HTTP (Watch Dog), make sure you sit back and relax
• Mobile Internet: mobile search, mobile mashups (mashup), mobile social networking, mobile commerce are inseparable from the structure of the data content, DataScraper efficiently capture real-time content, the output is rich semantic metadata XML format for the capture outcome document, to ensure that automated data integration and processing, across the small size screen display and high precision information retrieval obstacles. Mobile Internet is not a subset of Web but all, by building bridges MetaSeeker
• Competitive intelligence gathering/data mining: commonly known as Business Intelligence (Business Intelli Platform: |
Size: 4218880 |
Author:陈东 |
Hits:
Description: 基于数据挖掘算法的成绩分析系统
本系统运用J2EE技术架构，采用SSH + Ajax架构设计，选用B/S模式设计方案，使用MVC设计思想进行开发；同时采用SQL SERVER2000数据库管理系统对后台数据库进行管理-Application and Implementation of Data mining algorithms in the analysis of students’ Scores Platform: |
Size: 26141696 |
Author:lianglili |
Hits:
Description: Aprioritid算法,应用Java编译语言,这是关联规则数据挖掘的过程
-Aprioritid algorithm, the application of the Java compiler language, the thing data mining association rules of procedure
Platform: |
Size: 15360 |
Author:万伟 |
Hits:
Description: 这是一本将数据挖掘算法和数据挖掘实践完美结合起来的优秀教材。作者以其丰富的经验，对数据挖掘的概念和数据挖掘所有的技术（特别是机器学习）进行了深入浅出的介绍，并对应用机器学习工具进行数据挖掘给出了良好的建议。-This is a data mining algorithms and data mining practice, the perfect combination of quality materials. By its experience, the concept of data mining and data mining all of the technology (in particular, machine learning) were described in simple terms, application of machine learning and data mining tools gives good advice. Platform: |
Size: 5609472 |
Author:郑牟 |
Hits:
Description: weka.数据挖掘开源代码。并基于此做一个小型 的应用。代码很适合于数据挖掘的理论学习。-Data mining of open source code Weka. And based on this to do a small application. The code is suitable for data mining theory study. Platform: |
Size: 3072 |
Author:sssssss |
Hits:
Description: 数据挖掘能处理大量的非线性数据，需要引入决策树才正确地理解应用-Data mining can deal with a large number of nonlinear data, need to introduce the decision tree to correctly understand the application
Platform: |
Size: 2319360 |
Author:Cathy |
Hits:
Description: 探讨数据挖掘在火电厂优化运行中的研究与应用-Explore data mining in power plant operation optimization Research and Application Platform: |
Size: 38912 |
Author:姜翻 |
Hits:
Description: AprioriAll算法的基本思路
1) 排序阶段 利用客户标识customer 2id作为主关键字以及事务发生的时间transaction 2 time作为次关键字对数据库D排序,该步骤将原始的事务数据库转换成客户序列的数据库.
2) 发现频繁项集阶段 利用关联规则挖掘算法找出所有的频繁项目集.
3) 转换阶段 在已经转换的客户序列中,每一个事务被包含于该事物中的所大项目集来替换,如果一个序列不包含任何大项目集,则在已经转换的序列中不应该保留这项事务.
4) 序列阶段 利用核心算法找出所有的序列模式.
-Sequential pattern mining from the sequence found in the database as a sequence of frequent pattern, it is a kind of important data mining issues, has a very wide application, be used in customer buying behavior, including the analysis of network access mode of analysis, the scientific experiments Analysis, the early diagnosis of disease, natural disasters forecast, DNA sequences deciphered, and so on. The efficiency. In this paper, I was in the sequence pattern mining one of two algorithms to study, namely: Armorial and GSP algorithm. First on the sequence patterns of some basic concepts and principles. And demonstrate through concrete examples of the implementation of the algorithm, then reached into the grasp of understanding. Used vc again based on the programming language and Access database to achieve the end result of running the analysis and synthesis. Platform: |
Size: 2048 |
Author:hou ruilian |
Hits:
Search - data mining application