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2010 IEEE International Conference on Data Mining Workshops最新文献

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Sequence Alignment Based Analysis of Player Behavior in Massively Multiplayer Online Role-Playing Games (MMORPGs) 基于序列对齐的大型多人在线角色扮演游戏(mmorpg)玩家行为分析
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.166
Kyong Jin Shim, J. Srivastava
{"title":"Sequence Alignment Based Analysis of Player Behavior in Massively Multiplayer Online Role-Playing Games (MMORPGs)","authors":"Kyong Jin Shim, J. Srivastava","doi":"10.1109/ICDMW.2010.166","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.166","url":null,"abstract":"This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following key components: 1) sequence alignment-based player profile databases, 2) feature selection schemes and prediction model building, and 3) decision support model for determining inactive players.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131257835","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: 6
Ensemble-Based Method for Task 2: Predicting Traffic Jam 任务2:预测交通阻塞的集成方法
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.54
Jingrui He, Qing He, G. Swirszcz, Y. Kamarianakis, Richard D. Lawrence, Wei Shen, L. Wynter
{"title":"Ensemble-Based Method for Task 2: Predicting Traffic Jam","authors":"Jingrui He, Qing He, G. Swirszcz, Y. Kamarianakis, Richard D. Lawrence, Wei Shen, L. Wynter","doi":"10.1109/ICDMW.2010.54","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.54","url":null,"abstract":"In this paper, we describe our solution for ICDM 2010 Contest Task 2 (Jams), where the task is to predict future where the next traffic jams will occur in morning rush hour, given data gathered during the initial phase of this peak period. Our solution, which is based on an ensemble approach, finished Second in the final evaluation.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131414496","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
Contextual Sequential Pattern Mining 上下文顺序模式挖掘
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.182
Julien Rabatel, S. Bringay, P. Poncelet
{"title":"Contextual Sequential Pattern Mining","authors":"Julien Rabatel, S. Bringay, P. Poncelet","doi":"10.1109/ICDMW.2010.182","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.182","url":null,"abstract":"Traditional sequential patterns do not take into account additional contextual information since patterns extracted from data are usually general. By considering the fact that a pattern is associated with one specific context the decision expert can then adapt his strategy considering the type of customers. In this paper we propose to mine more precise patterns of the form \"young users buy products A and B then product C, while old users do not follow this same behavior\". By highlighting relevant properties of such contexts, we show how contextual sequential patterns can be extracted by mining the database in a concise manner. We conduct our experimental evaluation on real-world data and demonstrate performance issues.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132728843","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
Parametric Templates: A New Enzyme Active-Site Prediction Algorithm 参数模板:一种新的酶活性位点预测算法
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.176
Tsuyoshi Kato, Kazuhiro Suwa, N. Nagano
{"title":"Parametric Templates: A New Enzyme Active-Site Prediction Algorithm","authors":"Tsuyoshi Kato, Kazuhiro Suwa, N. Nagano","doi":"10.1109/ICDMW.2010.176","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.176","url":null,"abstract":"It is an important problem to find functionally analogous enzymes based on the local structures of active-sites. Conventional methods predict active-sites by computing the deviations from the local-structure templates with no statistical parameters. We present a new statistical algorithm that uses parametric templates to compute the deviations of local sites. The parameters of the templates are determined automatically from a set of known active-sites. In this work, promising experimental results are shown through comparison of parametric templates with conventional templates.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133486300","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: 1
Empirical Analysis: News Impact on Stock Prices Based on News Density 基于新闻密度的新闻对股价影响实证分析
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.124
Xiaodong Li, Xiaotie Deng, Feng Wang, Keren Dong
{"title":"Empirical Analysis: News Impact on Stock Prices Based on News Density","authors":"Xiaodong Li, Xiaotie Deng, Feng Wang, Keren Dong","doi":"10.1109/ICDMW.2010.124","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.124","url":null,"abstract":"Analyzing the latent relationship between parallel news articles and stock prices has become an important research issue which attracts more and more researchers¡¯ attention. It is believed that news articles have impact on prices. Many approaches address this issue either from the documents¡¯sentiment point of view or from the word frequency point of view. In this paper, we propose a new model which captures the density of news articles and mines the latent relationship by employing information entropy to explore the news impact on the market. An empirical study is conducted to analyze market news articles¡¯ impact on stock prices. We compare our results with the traditional model which is based on support vector machine (baseline). Experimental results show that our proposed news density model has a better performance on predicting relatively long term news impact.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133922694","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: 7
Traffic Velocity Prediction Using GPS Data: IEEE ICDM Contest Task 3 Report 基于GPS数据的交通速度预测:IEEE ICDM竞赛任务3报告
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.52
Wei Shen, Y. Kamarianakis, L. Wynter, Jingrui He, Qing He, Richard D. Lawrence, G. Swirszcz
{"title":"Traffic Velocity Prediction Using GPS Data: IEEE ICDM Contest Task 3 Report","authors":"Wei Shen, Y. Kamarianakis, L. Wynter, Jingrui He, Qing He, Richard D. Lawrence, G. Swirszcz","doi":"10.1109/ICDMW.2010.52","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.52","url":null,"abstract":"This report summarizes the methodologies and techniques we developed and applied for tackling task 3 of the IEEE ICDM Contest on predicting traffic velocity based on GPS data. The major components of our solution include 1) A pre-processing procedure to map GPS data to the network, 2) A K-nearest neighbor approach for identifying the most similar training hours for every test hour, and 3) A heuristic evaluation framework for optimizing parameters and avoiding over-fitting. Our solution finished Second in the final evaluation.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123148969","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: 11
Automated Prompting in a Smart Home Environment 智能家居环境中的自动提示
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.147
Barnan Das, Chao Chen, N. Dasgupta, D. Cook, Adriana M. Seelye
{"title":"Automated Prompting in a Smart Home Environment","authors":"Barnan Das, Chao Chen, N. Dasgupta, D. Cook, Adriana M. Seelye","doi":"10.1109/ICDMW.2010.147","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.147","url":null,"abstract":"With more older adults and people with cognitive disorders preferring to stay independently at home, prompting systems that assist with Activities of Daily Living (ADLs) are in demand. In this paper, with the introduction of “The PUCK”, we take the very first approach to automate a prompting system without any predefined rule set or user feedback. We statistically analyze realistic prompting data and devise a classifier from statistical outlier detection methods. Further, we devise a sampling technique to help with skewed and scanty data sets. We empirically find a class distribution that would be suitable for our work and validate our claims with the help of three classical machine learning algorithms.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125212565","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: 21
Clutter-Adaptive Visualization for Mobile Data Mining 移动数据挖掘的杂波自适应可视化
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.134
Brett Gillick, Hasnain AlTaiar, S. Krishnaswamy, J. Liono, Nicholas Nicoloudis, Abhijat Sinha, A. Zaslavsky, M. Gaber
{"title":"Clutter-Adaptive Visualization for Mobile Data Mining","authors":"Brett Gillick, Hasnain AlTaiar, S. Krishnaswamy, J. Liono, Nicholas Nicoloudis, Abhijat Sinha, A. Zaslavsky, M. Gaber","doi":"10.1109/ICDMW.2010.134","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.134","url":null,"abstract":"There is an emerging focus on real-time data stream analysis on mobile devices. While many mobile data stream mining algorithms have been developed in recent times, generic and scalable visualization techniques have not been presented. This paper presents the demonstration of our innovative clutter-adaptive cluster visualization technique for mobile devices. We have fully implemented this technique on the Google Android platform and provide demonstrations for different datasets: location (both real and synthetic), and stock-market (real).","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134144867","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
dMaximalCliques: A Distributed Algorithm for Enumerating All Maximal Cliques and Maximal Clique Distribution dMaximalCliques:一种枚举所有最大团和最大团分布的分布式算法
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.13
Li Lu, Yunhong Gu, R. Grossman
{"title":"dMaximalCliques: A Distributed Algorithm for Enumerating All Maximal Cliques and Maximal Clique Distribution","authors":"Li Lu, Yunhong Gu, R. Grossman","doi":"10.1109/ICDMW.2010.13","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.13","url":null,"abstract":"Clique detection and analysis is one of the fundamental problems in graph theory. However, as the size of graphs increases (e.g., those of social networks), it becomes difficult to conduct such analysis using existing sequential algorithms due to the computation and memory limitation. In this paper, we present a distributed algorithm, dMaximalCliques, which can obtain clique information from million-node graphs within a few minutes on an 80-node computer cluster. dMaximalCliques is a distributed algorithm for share-nothing systems, such as racks of clusters. We use very large scale real and synthetic graphs in the experimental studies to prove the efficiency of the algorithm. In addition, we propose to use the distribution of the size of maximal cliques in a graph (Maximal Clique Distribution) as a new measure for measuring the structural properties of a graph and for distinguishing different types of graphs. Meanwhile, we find that this distribution can be well fitted by lognormal distribution.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132525173","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: 30
Semi-supervised PLSA for Document Clustering 半监督PLSA用于文档聚类
2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.85
Lingfeng Niu, Yong Shi
{"title":"Semi-supervised PLSA for Document Clustering","authors":"Lingfeng Niu, Yong Shi","doi":"10.1109/ICDMW.2010.85","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.85","url":null,"abstract":"By utilizing the must-link or cannot-link pair wise constraints in data, semi-supervised clustering improves the performance of unsupervised clustering significantly. A number of semi-supervised clustering algorithms have been proposed to consider such pair wise constraints. However, most of them assign a hard label to each data item and produce little information about the cluster itself. In this work, we propose a Probabilistic Latent Semantic Analysis(PLSA) based semi-supervised algorithm for documents clustering by employing the must-link supervision between two documents, which is available in many real world data. The new algorithm can produce the soft cluster label assignment for each document as well as the probabilistic representation of latent topics in the cluster. No additional parameters need to be estimated besides the parameters in standard PLSA. This reduces the risk of over-fitting especially when the data is sparse. We provide the Expectation Maximization(EM) procedure for semi-supervised PLSA to determine the local optimal parameters that maximize the likelihood. To utilize multiple computation nodes for large scale data set, we also propose a distributed implementation of the EM procedure based on the MapReduce framework. Experimental results on public data set validate the effectiveness and efficiency of the new method.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132968509","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: 13
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