University campus is the most important place for life, study, activity and experience of contemporary college students. It is helpful for students to survive and develop to create the topic space of campus. Taking th...University campus is the most important place for life, study, activity and experience of contemporary college students. It is helpful for students to survive and develop to create the topic space of campus. Taking the topic space of college campuses in Lishui City of Zhejiang Province as an example, the current situations are analyzed through questionnaire survey and field visit. The results show that uni- versity campus space needs a clear topic; the demands are generally large for the topics of exchange and communication, learning and thinking, sports and leisure in all kinds of space; the creation of these types of topic spaces should focus on the peaceful environment, beautiful scenery, privacy of the space and WlFI coverage.展开更多
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Con...To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.展开更多
The purpose of this paper is to introduce and discuss the concept of topical functions on upward sets. We give characterizations of topical functions in terms of upward sets.
为了缓解社交网络热点话题生成的密集图数据导致存储的频繁读取和缓存空间浪费等问题,针对话题产生与消亡的演化更新规律,提出了基于话题热度演化加速度的缓存置换算法(cache replacement algorithm based on topic heat evolution acce...为了缓解社交网络热点话题生成的密集图数据导致存储的频繁读取和缓存空间浪费等问题,针对话题产生与消亡的演化更新规律,提出了基于话题热度演化加速度的缓存置换算法(cache replacement algorithm based on topic heat evolution acceleration,THEA-CR)。该算法首先对社交网络数据进行话题簇的实体划分,识别锚定目标。其次,计算话题热度演化加速度,对热点数据的优先级进行研判;最后设计双队列缓存置换策略,针对话题关注度和访问频率进行缓存空间的置换和更新。在新浪微博数据集中与经典的缓存置换算法进行大量对比实验,验证了所提算法具有较好的可行性与有效性。结果表明提出的THEA-CR算法能够在社交网络密集图数据的不同图查询操作中平均提升约31.4%的缓存命中率,并且缩短了约27.1%的查询响应时间。展开更多
模糊C均值聚类作为聚类的一种有效方法在数据挖掘和信息检索等领域得到广泛的应用,初始中心和初始隶属度矩阵的建立是决定模糊C均值聚类效果的关键.本文提出一种基于文本主题空间的模糊C均值聚类算法TS2FCM(Topic Sub-Space based Fuzzy...模糊C均值聚类作为聚类的一种有效方法在数据挖掘和信息检索等领域得到广泛的应用,初始中心和初始隶属度矩阵的建立是决定模糊C均值聚类效果的关键.本文提出一种基于文本主题空间的模糊C均值聚类算法TS2FCM(Topic Sub-Space based Fuzzy C-Means),通过对能够代表文本主题的关键短语(salient phrase)的提取来建立主题子空间,利用主题子空间中的文本向量来提取初始中心和初始隶属度矩阵.实验表明,TS2FCM取得了较好的聚类效果.展开更多
文摘University campus is the most important place for life, study, activity and experience of contemporary college students. It is helpful for students to survive and develop to create the topic space of campus. Taking the topic space of college campuses in Lishui City of Zhejiang Province as an example, the current situations are analyzed through questionnaire survey and field visit. The results show that uni- versity campus space needs a clear topic; the demands are generally large for the topics of exchange and communication, learning and thinking, sports and leisure in all kinds of space; the creation of these types of topic spaces should focus on the peaceful environment, beautiful scenery, privacy of the space and WlFI coverage.
基金The National Natural Science Foundation of China(No60672056)Open Fund of MOE-MS Key Laboratory of Multime-dia Computing and Communication(No06120809)
文摘To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.
文摘The purpose of this paper is to introduce and discuss the concept of topical functions on upward sets. We give characterizations of topical functions in terms of upward sets.
文摘为了缓解社交网络热点话题生成的密集图数据导致存储的频繁读取和缓存空间浪费等问题,针对话题产生与消亡的演化更新规律,提出了基于话题热度演化加速度的缓存置换算法(cache replacement algorithm based on topic heat evolution acceleration,THEA-CR)。该算法首先对社交网络数据进行话题簇的实体划分,识别锚定目标。其次,计算话题热度演化加速度,对热点数据的优先级进行研判;最后设计双队列缓存置换策略,针对话题关注度和访问频率进行缓存空间的置换和更新。在新浪微博数据集中与经典的缓存置换算法进行大量对比实验,验证了所提算法具有较好的可行性与有效性。结果表明提出的THEA-CR算法能够在社交网络密集图数据的不同图查询操作中平均提升约31.4%的缓存命中率,并且缩短了约27.1%的查询响应时间。
