Description: 让你的检索更精确——一种实现中文HTML文档自动聚类的方法-let you search more accurate -- A Chinese automatic HTML document clustering methods Platform: |
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Author:none |
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Description: 一个增量文本聚类的算法。
参考文献：
Wai-chiu Wong, Ada Wai-chee Fu, Incremental Document Clustering for Web Page Classification. -an incremental version of the clustering algorithm. References : Wai-chiu Wong, Ada Wai-CHEE Fu, Incremental Document Clustering for Web Page Classification. Platform: |
Size: 242794 |
Author:章明 |
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Description: 文档聚类评估程序，计算查全率和查准率以、F值，C++编程实现-Document Clustering assessment procedures, calculation, recall and precision rate, the value of F, C Programming Platform: |
Size: 1438 |
Author:rr |
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Description: 语义平滑文件模式聚类,代表了文本挖掘的前沿技术,和热门方向(英语原版)-semantic document clustering model, the representative of the Text Mining of advanced technology, and popular direction (English original) Platform: |
Size: 242999 |
Author:汤佩 |
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Description: A Comprehensive Comparison Study of Document
Clustering for a Biomedical Digital Library MEDLINE(综合比较研究文档聚类的医学数字图书馆检索)-A Comprehensive Comparison Study of Docum ent Clustering for a Biomedical Digital Librar y MEDLINE (Integrated Document Clustering comparative study of medical digital library retrieval) Platform: |
Size: 332257 |
Author:汤佩 |
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Description: 利用聚类算法分割图像，将维对分法只可将图像分为2部分，可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数，聚类中心为随机值，当然也可以采用其他参数，程序编译为EXE文件后速度还可以接受，但尚有改进的余地，那位高手有空修改的话，请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can only image is divided into two parts, the two values can be used as the source, K-means algorithm can be divided into images of arbitrary multi-part. Procedures used directly in R, G, B color as the characteristic parameters for the cluster center random value, of course, can also be used for other parameters, procedures EXE compiler to speed document acceptable, but there is still room for improvement, but the master of the time change, then please give me also made in the code. Platform: |
Size: 50176 |
Author:pbt |
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Description: 文档聚类评估程序，计算查全率和查准率以、F值，C++编程实现-Document Clustering assessment procedures, calculation, recall and precision rate, the value of F, C Programming Platform: |
Size: 1024 |
Author:rr |
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Description: 此文档是K类均值聚类和模拟退火结合的软硬件化分算法。众所周知，模拟退火算法的通用性，此算法是模拟退火的改进，较单纯的SA更优秀。-This document is a category K-means clustering and simulated annealing combination of hardware and software sub-algorithm. As we all know, the generic simulated annealing algorithm, this algorithm is the improvement of simulated annealing, a simpler SA better. Platform: |
Size: 65536 |
Author:石敏敏 |
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Description: 这个文件包里包含了几乎所有的聚类的工具箱，包含采用混合高斯模型的运动物体检测方面的源代码。-This document contains the bag almost all the clustering of the toolbox, including the use of Gaussian Mixture Model of Moving Objects for testing source code. Platform: |
Size: 124928 |
Author:齐欣 |
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Description: 文本聚类，VC编程实现，作为一种无监督的机器学习方法，聚类由于不需要训练过程，以及不需要预先对文档手工标注类别，因此具有一定的灵活性和较高的自动化处理能力-Text Clustering, VC programming, as an unsupervised machine learning method, clustering by eliminating the need for the training process, and do not need to manually pre-marked on the document type, and therefore a higher degree of flexibility and automation of processing power Platform: |
Size: 16384 |
Author:chengbo |
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Description: 文档分类，用K均值，加入了K的选择算法，不用人为设定聚类个数-Document classification, using K-means, joined the K of the selection algorithm, not the number of artificial clustering Platform: |
Size: 20480 |
Author:王 |
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Description: 文本聚类是基于相似性算法的自动聚类技术，自动对大量无类别的文档进行归类，把内容相近的文档归为一类，并自动为该类生成特征主题词。适用于自动生成热点舆论专题、重大新闻事件追踪、情报的可视化分析等诸多应用。
灵玖Lingjoin（www.lingjoin.com）基于核心特征发现技术，突破了传统聚类方法空间消耗大，处理时间长的瓶颈；不仅聚类速度快，而且准确率高，内存消耗小，特别适合于超大规模的语料聚类和短文本的语料聚类。
灵玖文档聚类组件的主要特色在于：
1、速度快：可以处理海量规模的网络文本数据，平均每小时处理至少50万篇文档；
2、聚类精准：Top N的聚类中心往往能反映出当时的时事热点，适合于舆情热点计算；与国际上以聚类见长的Autonomy公司技术相比，灵玖的各项指标远远领先，或许是灵玖更懂中文吧
3、精准排序：各个类别按照影响权重排序，每个类中的文档按照重要性排序；
4、可定制：可以定制类别数、类别中心。
5、开放式接口：灵玖文档聚类组件作为LJParser的一部分，采用灵活的开发接口，可以方便地融入到用户的业务系统中，可以支持各种操作系统，各类调用语言。
灵玖文档聚类可以应用于文本挖掘、知识管理、搜索聚类、舆情监测等多种应用中。
-Text clustering algorithm is based on the similarity of automatic clustering techniques, automatically a large number of non-classified categories of documents, the contents of the documents fall into a similar category, and automatically generate the features for this kind of keywords. For automatic generation of hot topics of public opinion, major news event tracking, information visualization analysis and many other applications.
Ling Jiu Lingjoin (www.lingjoin.com) found that based on the core features of technology, a breakthrough of traditional clustering method of space consumption, processing time is long bottlenecks not only the clustering speed and high accuracy, memory consumption is small, is particularly suitable for ultra-large-scale corpus clustering and short text corpus clustering.
Ling-Jiu document clustering component of the main features are:
1, fast: the size of the network can handle the massive text data, the average hourly processing at least 50 mil Platform: |
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Author:lingjoin |
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Description: 可以实现大批文档的聚类 代码实效性 有利于很长一篇文档的有效规整-Can achieve a large number of document clustering code effectiveness of a document is conducive to effective long regular Platform: |
Size: 2150400 |
Author:余果 |
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Description: 基于WEKA平台的文本聚类研究与实现
文本聚类是文本挖掘领域的一个重要研究分支，是聚类方法在文本处理领域的应用。本文对基于空间向量模型的文本聚类过程做了较深入的讨论和总结，利用文本语料库，基于数据挖掘工具研究并实现了文本聚类的过程。本文首先给出了文本聚类的思想和过程，回顾了文本聚类领域的已有成果，列举了文本聚类领域在特征表示、特征提取等方面的基础研究工作。另外，本文回顾了现有的文本聚类算法，以及常用的文本聚类效果评价指标。在研究了已有成果的基础上，本文利用20 Newsgroup文本语料库，针对向量空间表示模型，在开源的数据挖掘平台WEKA上实现了文本预处理和k-means聚类算法，并根据实际聚类效果，就文本表示、特征选择、特征降维、等方面提出优化方案。-Text clustering is an important field of text mining research branch, is the clustering in the field of text processing applications. In this paper, based on vector space model for text clustering process to do a more in-depth discussion and summary, the use of the text corpus, based on data mining tools to study and realize the document clustering process. This paper shows the ideas and text clustering process, reviewed the existing text clustering results of the field, citing the field of document clustering in the feature representation, feature extraction and other aspects of basic research. In addition, the paper reviews the existing text clustering algorithm, as well as common text clustering validity. In the study has been based on the results, we use 20 Newsgroup corpus, for the vector space representation model, in the WEKA open source data mining platform to achieve a text preprocessing and k-means clustering algorithm, and according to the actual clustering effect to the tex Platform: |
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Author:yueyue |
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Description: Text Document Clustering Document for the MCA and Mtech Students for the final year projects.This Document Clustering is based on several algorithms and full document is presented here. Platform: |
Size: 432128 |
Author:santosh |
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Search - Document clustering