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

bitSPADE: A Lattice-based Sequential Pattern Mining Algorithm Using Bitmap Representation bitSPADE:一种使用位图表示的基于格的顺序模式挖掘算法

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.28

S. Aseervatham, A. Osmani, E. Viennet

引用次数: 33

Object Identification with Constraints 带约束的对象识别

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.117

Steffen Rendle, L. Schmidt-Thieme

引用次数: 28

LOCI: Load Shedding through Class-Preserving Data Acquisition LOCI:通过保持类的数据采集来减少负载

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.100

Peng Wang, Haixun Wang, Wei Wang, Baile Shi, Philip S. Yu

引用次数: 0

Getting the Most Out of Ensemble Selection 最大限度地利用合奏选择

Sixth International Conference on Data Mining (ICDM'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDM.2006.76

R. Caruana, Art Munson, Alexandru Niculescu-Mizil

引用次数: 147