Seventh IEEE International Conference on Data Mining (ICDM 2007)最新文献_第2页

Exploration of Link Structure and Community-Based Node Roles in Network Analysis 网络分析中链路结构与社区节点角色的探索

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.37

J. Scripps, P. Tan, A. Esfahanian

引用次数: 61

Local Word Bag Model for Text Categorization 文本分类的局部词袋模型

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.69

Wen Pu, Ning Liu, Shuicheng Yan, Jun Yan, Kunqing Xie, Zheng Chen

{"title":"Local Word Bag Model for Text Categorization","authors":"Wen Pu, Ning Liu, Shuicheng Yan, Jun Yan, Kunqing Xie, Zheng Chen","doi":"10.1109/ICDM.2007.69","DOIUrl":"https://doi.org/10.1109/ICDM.2007.69","url":null,"abstract":"Many text processing applications adopted the bag of words (BOW) model representation of documents, in which each document is represented as a vector of weighted terms or n-grams, and then the cosine distance between two vectors is used as the similarity measurement. Although the great success in information retrieval and text categorization, the conventional BOW model ignores the detailed local text information, i.e. the co-occurrence pattern of words at sentence or paragraph level. In this paper, we propose a novel approach to represent a document as a set of local tf-idf vectors, or what we called local word bags (LWB). By encapsulating local information distributed around a document into multiple LWBs, we can measure the similarity of two documents via the partial match of their corresponding local bags. To perform the matching efficiently, we introduce the local word bag kernel (LWB kernel), a variant of VG-Pyramid match kernel. The new kernel enables the discriminative machine learning methods like SVM to compute the partial matching between two sets of LWBs in linear time after an one time hierarchical clustering procedure over all local bags at the initialization stage. Experiments on real world datasets demonstrate the effectiveness of our new approach.","PeriodicalId":233758,"journal":{"name":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123141427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Structure-Based Statistical Features and Multivariate Time Series Clustering 基于结构的统计特征与多元时间序列聚类

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.103

Xiaozhe Wang, Anthony Wirth, Liang Wang

引用次数: 76

Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks 趋势母题:动态复杂网络分析的图挖掘方法

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.92

R. Jin, Scott McCallen, E. Almaas

{"title":"Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks","authors":"R. Jin, Scott McCallen, E. Almaas","doi":"10.1109/ICDM.2007.92","DOIUrl":"https://doi.org/10.1109/ICDM.2007.92","url":null,"abstract":"Complex networks have been used successfully in scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Until recently, studies of complex networks have mainly focused on their network topology. However, in many real world applications, the edges and vertices have associated attributes that are frequently represented as vertex or edge weights. Furthermore, these weights are often not static, instead changing with time and forming a time series. Hence, to fully understand the dynamics of the complex network, we have to consider both network topology and related time series data. In this work, we propose a motif mining approach to identify trend motifs for such purposes. Simply stated, a trend motif describes a recurring subgraph where each of its vertices or edges displays similar dynamics over a user- defined period. Given this, each trend motif occurrence can help reveal significant events in a complex system; frequent trend motifs may aid in uncovering dynamic rules of change for the system, and the distribution of trend motifs may characterize the global dynamics of the system. Here, we have developed efficient mining algorithms to extract trend motifs. Our experimental validation using three disparate empirical datasets, ranging from the stock market, world trade, to a protein interaction network, has demonstrated the efficiency and effectiveness of our approach.","PeriodicalId":233758,"journal":{"name":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125871494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 55

Active Learning from Data Streams 从数据流主动学习

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.101

Xingquan Zhu, Peng Zhang, Xiaodong Lin, Yong Shi

引用次数: 116

Efficient Algorithms for Mining Significant Substructures in Graphs with Quality Guarantees 具有质量保证的图中重要子结构的高效挖掘算法

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.11

Huahai He, Ambuj K. Singh

引用次数: 21

Transitional Patterns and Their Significant Milestones 过渡模式及其重要里程碑

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.87

Qian Wan, Aijun An

{"title":"Transitional Patterns and Their Significant Milestones","authors":"Qian Wan, Aijun An","doi":"10.1109/ICDM.2007.87","DOIUrl":"https://doi.org/10.1109/ICDM.2007.87","url":null,"abstract":"Mining frequent patterns in transaction databases has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithms do not consider the time stamps associated with the transactions. In this paper, we extend the existing frequent pattern mining framework to take into account the time stamp of each transaction and discover patterns whose frequency dramatically changes over time. We define a new type of patterns, called transitional patterns, to capture the dynamic behavior of frequent patterns in a transaction database. Transitional patterns include both positive and negative transitional patterns. Their frequencies increase/decrease dramatically at some time points of a transaction database. We introduce the concept of significant milestones for a transitional pattern, which are time points at which the frequency of the pattern changes most significantly. Moreover, we develop an algorithm to mine from a transaction database the set of transitional patterns along with their significant milestones. Our experimental studies on real-world databases illustrate that mining positive and negative transitional patterns is highly promising as a practical and useful approach to discovering novel and interesting knowledge from large databases.","PeriodicalId":233758,"journal":{"name":"Seventh IEEE International Conference on Data Mining (ICDM 2007)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132495890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Finding Predictive Runs with LAPS 利用LAPS找到预测运行

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.84

Suhrid Balakrishnan, D. Madigan

引用次数: 8

Mechanism Design for Clustering Aggregation by Selfish Systems 自私系统聚类聚集的机制设计

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.80

Pinata Winoto, Yiu-ming Cheung, Jiming Liu

引用次数: 1

Analyzing and Detecting Review Spam 分析和检测评论垃圾邮件

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI: 10.1109/ICDM.2007.68

Nitin Jindal, B. Liu

引用次数: 232