Description: 搜索引擎结果聚类提纯
含cgi页面代码和后台处理代码
在聚类过程中将内容过于重复的页面合并，因此用户看到不同的文档将能够获取不同的信息。
-Purification clustering search engine results pages with cgi code and deal with code in the background during clustering repeat too merge pages, so users see different documents will be able to access different information. Platform: |
Size: 1071104 |
Author:sisn |
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Description: 目前的搜索引擎产生的搜索结果过于庞大和杂乱，用户难以从大量的结果集中快速找到自己感兴趣的信息。
为了便于用户浏览，利用文档聚类算法将搜索结果自动聚类，形成一个类似文件夹的层次结构是一种好的方法。
传统的文档聚类算法，所产生的聚类结果簇没有可读性，不适于直接应用于网页的聚类。
-The current generated by the search engine search results is too large and cluttered, the user can hardly concentrate on the results from a large number quickly find information of interest to them. In order to facilitate users to browse, use the document search results clustering algorithm auto-clustering, create a similar folder hierarchy is a good method. Traditional document clustering algorithm, clustering results generated by the cluster is not readable, not suitable for directly applied to the cluster page. Platform: |
Size: 59392 |
Author:林健 |
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Description: Carrot2 is an Open Source Search Results Clustering Engine. It can automatically organize (cluster) search results into thematic categories. Platform: |
Size: 4096 |
Author:如此风格 |
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Description: Oracle Text uses standard SQL to index, search, and analyze text and documents stored in the Oracle database, in files, and on the web. Oracle Text can perform linguistic analysis on documents, as well as search text using a variety of strategies including keyword searching, context queries, Boolean operations, pattern matching, mixed thematic queries, HTML/XML section searching, and so on. It can render search results in various formats including unformatted text, HTML with term highlighting, and original document format. Oracle Text supports multiple languages and uses advanced relevance-ranking technology to improve search quality. Oracle Text also offers advanced features like classification, clustering, and support for information visualization metaphors. Platform: |
Size: 163840 |
Author:eranisrikantha |
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Description: 为寻求复杂多峰函数的全局最优解问题, 提出了新型混合算法。该算法由带共享函数
的遗传算法、移民技术、聚类算法和改进的Pow ell 算法组成。由于上述算法的有机配合, 提高了
混合算法的全局和局部搜索能力。油藏系统应用实例和仿真实例证明了算法的有效性-Complex multimodal function to find a global optimal solution of problem, a new hybrid algorithm. The algorithm functions by a genetic algorithm with sharing, migration technology, clustering algorithm and the improved algorithm is composed of Pow ell. As the organic with the above algorithm, a hybrid algorithm to improve the global and local search capabilities. Reservoir system application examples and simulation results demonstrate the effectiveness of the algorithm Platform: |
Size: 346112 |
Author:赵 |
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Description: A fault identification with fuzzy C-Mean clustering
algorithm based on improved ant colony algorithm (ACA) is
presented to avoid local optimization in iterative process of
fuzzy C-Mean (FCM) clustering algorithm and the difficulty in
fault classification. In the algorithm, the problem of fault
identification is translated to a constrained optimized
clustering problem. Using heuristic search of colony can find
good solutions. And according to the information content of
cluster center, it could merger surrounding data together to
cause clustering identification. The algorithm may identify
fuzzy clustering numbers and initial clustering center. It can
also prevent data classification from causing some errors.
Thus, applying in fault diagnosis shows validity of computing
and credibility of identification results. Platform: |
Size: 273408 |
Author:rishi |
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Description: 针对传统推荐算法的数据稀疏性问题和推荐准确性问题，提出基于粒子群优化的项聚类推荐算法。采用粒子群优化算法产生聚类中心，在此基础上搜索目标项目的最近邻居，并产生推荐，从而提高了传统聚类算法的推荐准确性及响应速度。实验表明改进的项聚类协同过滤算法能有效提高推荐精度-Aiming at the problems that the data are sparse and the results are not accurate in traditional recommendation algorithms, this paper proposes an item clustering recommendation algorithm based on Particle Swarm Optimization(PSO) algorithm. It uses PSO to engender the cluster centers, calculates the similarity between target item and cluster centers to search the nearest neighbors of target item, and gains a recommendation, so that it improves the accuracy and the real-time performance. Experimental results indicate that the algorithm can effectively improve the accuracy of the recommendation system Platform: |
Size: 366592 |
Author:ming |
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Description: Search Results Clustering in Chinese Context Based on a New Suffix Tr-Search Results Clustering in Chinese Context Based on a New Suffix Tree Platform: |
Size: 368640 |
Author:arafatalawy |
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Description: 抽取百度的搜索结果并使用基于最长公共子串的方法进行在线聚类。-Extraction and use Baidu' s search results based on the longest common substring approach to online clustering. Platform: |
Size: 103424 |
Author:王悦 |
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Description: 基于相似度线性加权方法的检索结果聚类研究-The linear weighted method based on similarity search results clustering research Platform: |
Size: 502784 |
Author:林大 |
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Description: In this paper, a novel Discrete Particle Swarm Optimization Algorithm
(DPSOA) for data clustering has been proposed. The particle positions and velocities
are defined in a discrete form. The DPSOA algorithm uses of a simple probability
approach to construct the velocity of particle followed by a search scheme to
constructs the clustering solution. DPSOA algorithm has been applied to solve the
data clustering problems by considering two performance metrics, such as TRace
Within criteria (TRW) and Variance Ratio Criteria (VRC). The results obtained by
the proposed algorithm have been compared with the published results of Basic
PSO (B-PSO) algorithm, Genetic Algorithm (GA), Differential Evolution (DE) algorithm
and Combinatorial Particle Swarm Optimization (CPSO) algorithm. The
performance analysis demonstrates the effectiveness of the proposed algorithm in
solving the partitional data clustering problems. Platform: |
Size: 195584 |
Author:ali |
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Description: carrot2是一款开源的聚类可视化搜索引擎，并提供了java API以供开发使用。内部包含所有用于carrot2开发的jar包和实例。-You can use Carrot2 Java API to fetch documents various sources (public search engines, Lucene, Solr), perform clustering, serialize the results to JSON or XML and many more. Below is some example code for the most common use cases. Please see the examples/ directory in the Java API distribution archive for more examples. Platform: |
Size: 24756224 |
Author:haowei |
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Description: Python密度聚类
最近在Science上的一篇基于密度的聚类算法《Clustering by fast search and find of density peaks》引起了大家的关注(在我的博文“论文中的机器学习算法——基于密度峰值的聚类算法”中也进行了中文的描述)。于是我就想了解下基于密度的聚类算法，熟悉下基于密度的聚类算法与基于距离的聚类算法，如K-Means算法之间的区别。
基于密度的聚类算法主要的目标是寻找被低密度区域分离的高密度区域。与基于距离的聚类算法不同的是，基于距离的聚类算法的聚类结果是球状的簇，而基于密度的聚类算法可以发现任意形状的聚类，这对于带有噪音点的数据起着重要的作用。(The main goal of the density based clustering algorithm is to find high density regions separated by low density regions. Different from distance based clustering algorithm, the clustering results based on distance clustering algorithm are spherical clusters, and density based clustering algorithm can detect clustering of arbitrary shapes, which plays an important role in data with noisy points.) Platform: |
Size: 10240 |
Author:cjh1882 |
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Search - clustering search results