2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第4页

Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system 代表全球气候系统的复杂网络中相关测度和修剪水平的实证比较

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949305

Alex Pelan, K. Steinhaeuser, N. Chawla, D. Pitts, A. Ganguly

{"title":"Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system","authors":"Alex Pelan, K. Steinhaeuser, N. Chawla, D. Pitts, A. Ganguly","doi":"10.1109/CIDM.2011.5949305","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949305","url":null,"abstract":"Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. One way of studying the dynamics of the Earth's changing climate is by attempting to identify regions that exhibit similar climatic behavior in terms of long-term variability. Climate networks have emerged as a strong analytics framework for both descriptive analysis and predictive modeling of the emergent phenomena. Previously, the networks were constructed using only one measure of similarity, namely the (linear) Pearson cross correlation, and were then clustered using a community detection algorithm. However, nonlinear dependencies are known to exist in climate, which begs the question whether more complex correlation measures are able to capture any such relationships. In this paper, we present a systematic study of different univariate measures of similarity and compare how each affects both the network structure as well as the predictive power of the clusters.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123809216","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: 8

User-guided discovery of declarative process models 用户引导的声明性流程模型发现

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949297

F. Maggi, A. Mooij, Wil M.P. van der Aalst

{"title":"User-guided discovery of declarative process models","authors":"F. Maggi, A. Mooij, Wil M.P. van der Aalst","doi":"10.1109/CIDM.2011.5949297","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949297","url":null,"abstract":"Process mining techniques can be used to effectively discover process models from logs with example behaviour. Cross-correlating a discovered model with information in the log can be used to improve the underlying process. However, existing process discovery techniques have two important drawbacks. The produced models tend to be large and complex, especially in flexible environments where process executions involve multiple alternatives. This “overload” of information is caused by the fact that traditional discovery techniques construct procedural models explicitly showing all possible behaviours. Moreover, existing techniques offer limited possibilities to guide the mining process towards specific properties of interest. These problems can be solved by discovering declarative models. Using a declarative model, the discovered process behaviour is described as a (compact) set of rules. Moreover, the discovery of such models can easily be guided in terms of rule templates. This paper uses DECLARE, a declarative language that provides more flexibility than conventional procedural notations such as BPMN, Petri nets, UML ADs, EPCs and BPEL. We present an approach to automatically discover DECLARE models. This has been implemented in the process mining tool ProM. Our approach and toolset have been applied to a case study provided by the company Thales in the domain of maritime safety and security.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122322019","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: 152

Local neighbourhood extension of SMOTE for mining imbalanced data 非平衡数据挖掘SMOTE的局部邻域扩展

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949434

Tomasz Maciejewski, J. Stefanowski

引用次数: 218

Data mining driven agents for predicting online auction's end price 数据挖掘驱动代理预测在线拍卖最终价格

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949427

Preetinder Kaur, M. Goyal, Jie Lu

引用次数: 11

Computational intelligence methods for processing misaligned, unevenly sampled time series containing missing data 处理包含缺失数据的不对齐、不均匀采样时间序列的计算智能方法

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949447

F. Cismondi, André S. Fialho, S. Vieira, J. Sousa, S. Reti, M. Howell, S. Finkelstein

{"title":"Computational intelligence methods for processing misaligned, unevenly sampled time series containing missing data","authors":"F. Cismondi, André S. Fialho, S. Vieira, J. Sousa, S. Reti, M. Howell, S. Finkelstein","doi":"10.1109/CIDM.2011.5949447","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949447","url":null,"abstract":"One consequence of the increasing amount of data stored during acquisition processes is that sampled time series are more prone to be collected in a misaligned uneven fashion and/or be partly lost or unavailable (missing data). Due to their severe impact on data mining techniques, this work proposes methods to (a) align misaligned unevenly sampled data, (b) differentiate absent values related to low sampling frequencies, compared to those resulting from missingness mechanisms, and (c) to classify recoverable and non-recoverable segments of missing data by using statistical and fuzzy modeling approaches. These methods were evaluated against randomly simulated test datasets containing different amounts of missing data. Results show that: (1) using the variable most frequently sampled as a template, combined with cubic interpolation, allowed to unshift misaligned uneven data without significant errors; (2) the differentiation of absent values due to low sampling frequencies from those truly missing, can be succesfully performed using 95% confidence intervals relative to the mean sampling time; (3) fuzzy modeling returned better classification results for recoverable segments, while the statistical approach performed better in classifying non-recoverable segments. All three methods proposed in this work decreased their performance when the amount of missing data was increased in the test datasets.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116033706","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: 33

Using gaming strategies for attacker and defender in recommender systems 在推荐系统中使用攻击者和防御者的游戏策略

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949304

J. Zhan, Lijo Thomas, Venkata Pasumarthi

引用次数: 4

Generating materialized views using ant based approaches and information retrieval technologies 使用基于蚁群的方法和信息检索技术生成物化视图

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949302

H. Drias

引用次数: 5

Enhancing precision in Process Conformance: Stability, confidence and severity 提高工艺一致性的精度:稳定性、信心和严谨性

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949451

J. Munoz-Gama, J. Carmona

引用次数: 74

Sectors on sectors (SonS): A new hierarchical clustering visualization tool 扇区上扇区(SonS):一种新的分层聚类可视化工具

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949448

J. Martínez-Martínez, Pablo Escandell-Montero, E. Soria-Olivas, J. Martín-Guerrero, M. Martínez-Sober, J. Gómez-Sanchís

{"title":"Sectors on sectors (SonS): A new hierarchical clustering visualization tool","authors":"J. Martínez-Martínez, Pablo Escandell-Montero, E. Soria-Olivas, J. Martín-Guerrero, M. Martínez-Sober, J. Gómez-Sanchís","doi":"10.1109/CIDM.2011.5949448","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949448","url":null,"abstract":"Clustering techniques have been widely applied to extract information from high-dimensional data structures in the last few years. Graphs are especially relevant for clustering, but many graphs associated with hierarchical clustering do not give any information about the values of the centroids' attributes and the relationships among them. In this paper, we propose a new visualization approach for hierarchical cluster analysis in which the above-mentioned information is available. The method is based on pie charts. The pie charts are divided into several pie segments or sectors corresponding to each cluster. The radius of each pie segment is proportional to the number of patterns included in each cluster. By means of new divisions in each pie sector and a color bar with as many labels as attributes, we can extract all the existing relationships among centroids' attributes at any hierarchy level. The methodology is tested in one synthetic data set and one real data set. Achieved results show the suitability and usefulness of the proposed approach.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131020493","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: 5

A banner recommendation system based on web navigation history 基于网页导航历史的banner推荐系统

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949437

G. Giuffrida, D. Recupero, Giuseppe Tribulato, C. Zarba

引用次数: 4