Comparing PSO-based clustering over contextual vector embeddings to modern topic modeling     

《Information processing & management》2022,59(3):102921

Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In the present paper, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on two datasets. The first dataset consists of posts from the online health forum r/Cancer and the second dataset is a standard benchmark for topic modeling which consists of a collection of messages posted to 20 different news groups. When compared to the state-of-the-art generative document models (i.e., ETM and NVDM), pPSO is able to produce interpretable clusters. The results indicate that pPSO is able to capture both common topics as well as emergent topics. Moreover, the topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20NewsGroups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus-specific vocabulary which is used by ETM and NVDM.  相似文献   

基于迭代自组织数据聚类阈值PCNN的图像分割法     

曾海妮  彭建盛 《河池学院学报》2014,(2):71-76

基于迭代自组织数据聚类阈值的脉冲耦合神经网络的图像分割算法改进了传统脉冲耦合神经网络在图像分割中由于不恰当的参数选择而导致图像欠分割和过分割的问题.基于迭代自组织数据聚类阈值的脉冲耦合神经网络图像分割算法无需确定参数和循环次数,也不需要用特定原则确定循环结束的条件,只需利用图像中的每个像素点的灰度值进行聚类,然后利用改进的迭代自组织数据算法确定图像的初始聚类数目以及聚类中心,并以此作为脉冲耦合神经网络的最佳阈值,一次点火过程自动完成分割.实验结果表明,这种算法具有较好的分割结果和分割速度,提高了分割的准确性.  相似文献   

国际大数据研究主题的可视化分析     

王一博  ;郭鑫  ;王继民 《数字图书馆论坛》2014,(7):52-57

随着大数据时代的来临,有关大数据的理论、技术、方法与应用的研究已成为当前产、学、研的研究热点。以Web of Science数据库的文献信息为数据源,对大数据领域的论文关键词进行共词分析,构建高频关键词共现关系网络,可视化地展示该网络的“核心-边缘结构”,通过聚类分析将这一领域的研究内容划分为14个类团,并利用战略坐标图揭示该领域的各个研究主题及其发展趋势,以期为相关研究提供参考。  相似文献   

Crime profiling for the Arabic language using computational linguistic techniques     

Meshrif Alruily  Aladdin Ayesh  Hussein Zedan 《Information processing & management》2014

Arabic is a widely spoken language but few mining tools have been developed to process Arabic text. This paper examines the crime domain in the Arabic language (unstructured text) using text mining techniques. The development and application of a Crime Profiling System (CPS) is presented. The system is able to extract meaningful information, in this case the type of crime, location and nationality, from Arabic language crime news reports. The system has two unique attributes; firstly, information extraction that depends on local grammar, and secondly, dictionaries that can be automatically generated. It is shown that the CPS improves the quality of the data through reduction where only meaningful information is retained. Moreover, the Self Organising Map (SOM) approach is adopted in order to perform the clustering of the crime reports, based on crime type. This clustering technique is improved because only refined data containing meaningful keywords extracted through the information extraction process are inputted into it, i.e. the data are cleansed by removing noise. The proposed system is validated through experiments using a corpus collated from different sources; it was not used during system development. Precision, recall and F-measure are used to evaluate the performance of the proposed information extraction approach. Also, comparisons are conducted with other systems. In order to evaluate the clustering performance, three parameters are used: data size, loading time and quantization error.  相似文献   

一种改进的基于粒子群的离线聚类算法     

张英武 《鞍山师范学院学报》2013,(4):46-49

为了解决K-means算法中对于初值的敏感,提出了一种基于粒子群的改进的K-means聚类算法(IPSOFCM).在K-means算法中引入粒子群算法,可有效提高算法的全局搜索能力,有助于粒子更容易跳出局部束缚.实验结果证明,IPSOFCM算法聚类准确度高,稳定性好.  相似文献   

融合量化粗糙度的改进的图像分割方法     

谢琪  王加阳 《人天科学研究》2011,10(7):149-151

结合粗糙集理论,利用像素邻域的空间信息,可以构造图像色彩分布的上下近似以及量化粗糙性表示,据此提出一种基于量化粗糙信息的改进的图像分割方法,该方法使用局部量化粗糙度和待定算子来更新FCM算法中的隶属度函数。通过对比传统的模糊C-均值（FCM）聚类分割算法,证明该方法大大降低了时间复杂度,且具有良好的分割效果。  相似文献   

Cluster searching strategies for collaborative recommendation systems     

Ismail Sengor Altingovde  Özlem Nurcan Subakan  Özgür Ulusoy 《Information processing & management》2013

In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures.  相似文献   

A generalized cluster centroid based classifier for text categorization     

Guansong Pang  Shengyi Jiang 《Information processing & management》2013

In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two well-known classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is that we employ the improved Rocchio model to speed up the categorization process of KNN. Extensive experiments conducted on both English and Chinese corpora show that GCCC and its variants have better categorization ability than some state-of-the-art classifiers, i.e., Rocchio, KNN and Support Vector Machine (SVM).  相似文献   

Evaluating subtopic retrieval methods: Clustering versus diversification of search results     

Claudio Carpineto  Massimiliano D’AmicoGiovanni Romano 《Information processing & management》2012

To address the inability of current ranking systems to support subtopic retrieval, two main post-processing techniques of search results have been investigated: clustering and diversification. In this paper we present a comparative study of their performance, using a set of complementary evaluation measures that can be applied to both partitions and ranked lists, and two specialized test collections focusing on broad and ambiguous queries, respectively. The main finding of our experiments is that diversification of top hits is more useful for quick coverage of distinct subtopics whereas clustering is better for full retrieval of single subtopics, with a better balance in performance achieved through generating multiple subsets of diverse search results. We also found that there is little scope for improvement over the search engine baseline unless we are interested in strict full-subtopic retrieval, and that search results clustering methods do not perform well on queries with low divergence subtopics, mainly due to the difficulty of generating discriminative cluster labels.  相似文献   

基于概念格的数字图书馆用户市场细分* ——数字图书馆用户的概念聚类分析   总被引：1，自引：0，他引：1  

滕广青  毕强 《现代图书情报技术》2010,26(2):7-11

以概念格理论为基础,借助营销学中市场细分的变量,通过概念聚类,用形式概念分析的方法对数字图书馆用户进行市场细分。对在数字图书馆用户细分中突破传统统计口径、建立可伸缩的细分机制进行探索。  相似文献