Description: web流数据聚类挖掘的研究和应用。流数据作为一种特殊的可挖掘的数据类型，特别是基于网络技术迅速发展的今天，其彰显出的重要地位决定了它的挖掘技术需要被认真考虑，而如何在流数据中进行有效的聚类分析，是一个吸引研究者很大注意力的问题。-Clustering web streaming data mining research and applications. Flow data as a special type of data that can be excavated, in particular the rapid development of Web-based technology today, it underlines the important status of the decision of its mining technology need to be seriously considered, and how to stream data effectively together type of analysis, is a great to attract the attention of researchers question. Platform: |
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Author:昂森 |
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Description: 本书考察数据挖掘的概念和技术。数据挖掘是数据和信息系统及其应用的一个有希望的、欣欣向荣的学科前沿。数据挖掘通常又称为数据中的知识发现（KDD），是自动或方便地提取代表知识的模式；这些模式隐藏或记录在大型数据库、数据仓库、Web、其他大量信息库或数据流中。
-Book study the concept of data mining and technology. Data mining is the data and information systems and their applications a promising and flourishing frontier disciplines. Data mining are usually referred to as Knowledge Discovery in Data (KDD), is automatic or easy to extract the representative of the knowledge model these models hide or recorded in large databases, data warehouse, Web, other substantial information database or data stream. Platform: |
Size: 1786880 |
Author:马西亚 |
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Description: 改代码为fp-growth的源代码，用于数据挖掘中的关联规则挖掘，已经通过调试，可以放心使用，只需要加入输入文件流-Change the code for the fp-growth of the source code for the data mining association rule mining have been through testing, you can rest assured that the use, only need to join the input file stream Platform: |
Size: 873472 |
Author:胡飞 |
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Description: 分析流数据的数据挖掘算法，以及一个流数据的模拟产生器。-Analysis of the data stream of data mining algorithms, as well as a stream of data analog generator. Platform: |
Size: 1671168 |
Author:Johnny |
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Description: 供应链管理实用建模方法及数据挖掘
本书包括：经济全球化及企业竞争优势、供应链管理理论、供应链本流及其定量分析方法、提高供应链管理的运作绩效等内容。
简明目录：
1.经济全球化及企业竞争优势/2.供应链管理理论/3.供应链成本流及其定量分析方法/4.提高供应链管理的运作绩效/5.几种数据驱动型建模方法/6.产品供应链中的数据挖掘技术/7.供应链契约//参考文献-Supply Chain Management Modeling and Data Mining practical book include: Economic Globalization and competitive advantage, supply chain management, supply chain and the quantitative analysis of the flow, improve operational performance in supply chain management and so on. Concise catalog: 1. Economic globalization and competitive advantage/2. Supply chain management theory/3. Supply chain cost stream and its quantitative analysis/4. Improve the operational performance of supply chain management/5. Several data-driven construction model approach/6. product supply chain data mining/7. Supply Chain Contract// References Platform: |
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Author:showna |
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Description:
Stream Data Mining
Data mining is a part of a process called KDD-knowledge discovery in databases. This process consists basically
of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing,
and data transformation. Association rule techniques are used for data mining if the goal is to detect relationships or associations
between specific values of categorical variables in large data sets. Platform: |
Size: 361472 |
Author:Hamid |
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Description: 界标窗口中数据流频繁模式挖掘算法研究A window in the data stream frequent pattern mining algorithm-A window in the data stream frequent pattern mining algorithm Platform: |
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Author: |
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Description: In this era an emerging filed in the data mining is data stream mining. The current research technique of the data stream is classification which mainly focuses on concept drift data. In mining drift data with the single classifier is not sufficient for classifying the data. Because of the high dimensionality and does not get processed within considerable time, memory, false alarm rate is high, classification accuracy result is low. In this paper, proposed a Genetic based Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. The proposed method achieves improvement in classification accuracy and perhaps to select the least number of features which show the way to simplification of learning task. The experimental shows that the advocated method performs well when compared with existing methods. Platform: |
Size: 107520 |
Author:Opencvresearcher |
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Description: 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 important research contents in data mining. The clustering criteria are summarized. The research status and progress of five traditional clustering algorithms are summarized comprehensively. Some new clustering algorithms are used. By combing, according to the sample attribution relationship, sample data preprocessing, sample similarity measure, sample update strategy, sample high-dimensionality and fusion with other disciplines, nearly 20 new algorithms in clustering, Such as granular clustering, uncertain clustering, quantum clustering, nuclear clustering, spectral clustering, clustering integration, concept clustering, spherical shell clustering, affine clustering, data stream clustering, etc., respectively Generalization.) Platform: |
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Author:mwlmwl |
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Search - data stream mining