Sixth International Conference on Data Mining (ICDM'06)最新文献_第3页

Probabilistic Enhanced Mapping with the Generative Tabular Model 基于生成表格模型的概率增强映射

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.128

R. Priam, M. Nadif

引用次数: 0

Improving Nearest Neighbor Classifier Using Tabu Search and Ensemble Distance Metrics 利用禁忌搜索和集合距离度量改进最近邻分类器

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.86

M. Tahir, Jim E. Smith

{"title":"Improving Nearest Neighbor Classifier Using Tabu Search and Ensemble Distance Metrics","authors":"M. Tahir, Jim E. Smith","doi":"10.1109/ICDM.2006.86","DOIUrl":"https://doi.org/10.1109/ICDM.2006.86","url":null,"abstract":"The nearest-neighbor (NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this paper, a new ensemble technique is proposed to improve the performance of NN classifier. The proposed approach combines multiple NN classifiers, where each classifier uses a different distance function and potentially a different set of features (feature vector). These feature vectors are determined for each distance metric using Simple Voting Scheme incorporated in Tabu Search (TS). The proposed ensemble classifier with different distance metrics and different feature vectors (TS-DF/NN) is evaluated using various benchmark data sets from UCI Machine Learning Repository. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without Feature Selection (FS)).","PeriodicalId":356443,"journal":{"name":"Sixth International Conference on Data Mining (ICDM'06)","volume":"27 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017535","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: 15

Incremental Mining of Frequent Query Patterns from XML Queries for Caching 基于缓存的XML查询频繁查询模式的增量挖掘

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.88

Guoliang Li, Jianhua Feng, Jianyong Wang, Yong Zhang, Lizhu Zhou

{"title":"Incremental Mining of Frequent Query Patterns from XML Queries for Caching","authors":"Guoliang Li, Jianhua Feng, Jianyong Wang, Yong Zhang, Lizhu Zhou","doi":"10.1109/ICDM.2006.88","DOIUrl":"https://doi.org/10.1109/ICDM.2006.88","url":null,"abstract":"Existing studies for mining frequent XML query patterns mainly introduce a straightforward candidate generate-and-test strategy and compute frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XML query patterns transformed from user queries. However, it is nontrivial to maintain such discovered frequent patterns in real XML databases because there may incur frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent ones. Accordingly, existing proposals are inefficient for the evolution of the transaction database. To address these problems, this paper presents an efficient algorithm IPS-FXQPMiner for mining frequent XML query patterns without candidate maintenance and costly tree-containment checking. We transform XML queries into sequences through a one- to-one mapping and then mine the frequent sequences to generate frequent XML query patterns. More importantly, based on IPS-FXQPMiner, an efficient incremental algorithm, Incre-FXQPMiner is proposed to incrementally mine frequent XML query patterns, which can minimize the I/O and computation requirements for handling incremental updates. Our experimental study on various real-life datasets demonstrates the efficiency and scalability of our algorithms over previous known alternatives.","PeriodicalId":356443,"journal":{"name":"Sixth International Conference on Data Mining (ICDM'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128139391","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: 9

COALA: A Novel Approach for the Extraction of an Alternate Clustering of High Quality and High Dissimilarity COALA:一种提取高质量和高不相似度交替聚类的新方法

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.37

Eric Bae, J. Bailey

引用次数: 140

Applying Data Mining to Pseudo-Relevance Feedback for High Performance Text Retrieval 将数据挖掘应用于伪相关反馈的高性能文本检索

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.22

Xiangji Huang, Y. Huang, M. Wen, Aijun An, Y. Liu, Josiah Poon

引用次数: 47

Solution Path for Semi-Supervised Classification with Manifold Regularization 具有流形正则化的半监督分类解路径

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.150

G. Wang, Tao Chen, D. Yeung, F. Lochovsky

引用次数: 12

Dirichlet Aspect Weighting: A Generalized EM Algorithm for Integrating External Data Fields with Semantically Structured Queries by Using Gradient Projection Method Dirichlet方面加权:一种利用梯度投影法集成外部数据域和语义结构化查询的广义EM算法

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.55

A. Velivelli, Thomas S. Huang

引用次数: 0

An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task 协同推荐任务图核的实验研究

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.18

François Fouss, Luh Yen, A. Pirotte, M. Saerens

{"title":"An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task","authors":"François Fouss, Luh Yen, A. Pirotte, M. Saerens","doi":"10.1109/ICDM.2006.18","DOIUrl":"https://doi.org/10.1109/ICDM.2006.18","url":null,"abstract":"This work presents a systematic comparison between seven kernels (or similarity matrices) on a graph, namely the exponential diffusion kernel, the Laplacian diffusion kernel, the von Neumann kernel, the regularized Laplacian kernel, the commute time kernel, and finally the Markov diffusion kernel and the cross-entropy diffusion matrix - both introduced in this paper - on a collaborative recommendation task involving a database. The database is viewed as a graph where elements are represented as nodes and relations as links between nodes. From this graph, seven kernels are computed, leading to a set of meaningful proximity measures between nodes, allowing to answer questions about the structure of the graph under investigation; in particular, recommend items to users. Cross- validation results indicate that a simple nearest-neighbours rule based on the similarity measure provided by the regularized Laplacian, the Markov diffusion and the commute time kernels performs best. We therefore recommend the use of the commute time kernel for computing similarities between elements of a database, for two reasons: (1) it has a nice appealing interpretation in terms of random walks and (2) no parameter needs to be adjusted.","PeriodicalId":356443,"journal":{"name":"Sixth International Conference on Data Mining (ICDM'06)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132293604","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: 129

Cluster Analysis of Time-Series Medical Data Based on the Trajectory Representation and Multiscale Comparison Techniques 基于轨迹表示和多尺度比较技术的时间序列医疗数据聚类分析

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.33

S. Hirano, S. Tsumoto

引用次数: 35

Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints 基于知识约束的最大广义频繁地理模式挖掘

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.110

V. Bogorny, J. Valiati, S. D. S. Camargo, P. Engel, B. Kuijpers, L. Alvares

引用次数: 26