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Semantic Analysis Method for Unstructured Data in Telecom Services 电信业务中非结构化数据的语义分析方法
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.79
M. Iwashita, K. Nishimatsu, S. Shimogawa
{"title":"Semantic Analysis Method for Unstructured Data in Telecom Services","authors":"M. Iwashita, K. Nishimatsu, S. Shimogawa","doi":"10.1109/ICDMW.2008.79","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.79","url":null,"abstract":"A variety of services have recently been provided depending on highly developed networks and personal equipment. With these advances, connecting this equipment has become increasingly more complicated. Problems such as an increase in no-connection and determining the cause have become difficult in some cases because software is often updated to keep up with advancements in services or security. Telecom operators must understand the situation and act as quickly as possible when they receive customer enquiries. In this paper, we propose a method for analyzing and classifying customer enquiries that enables quick and efficient responses. This method is based upon a dependency parsing and co-occurrence technique to enable classification of a large amount of unstructured data into patterns because customer enquiries are generally stored as unstructured textual data.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125406914","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: 3
Data Mining for Climate Change and Impacts 气候变化及其影响的数据挖掘
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.30
A. Ganguly, K. Steinhaeuser
{"title":"Data Mining for Climate Change and Impacts","authors":"A. Ganguly, K. Steinhaeuser","doi":"10.1109/ICDMW.2008.30","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.30","url":null,"abstract":"Knowledge discovery from temporal, spatial and spatiotemporal data is critical for climate change science and climate impacts. Climate statistics is a mature area. However, recent growth in observations and model outputs, combined with the increased availability of geographical data, presents new opportunities for data miners. This paper maps climate requirements to solutions available in temporal, spatial and spatiotemporal data mining. The challenges result from long-range, long-memory and possibly nonlinear dependence, nonlinear dynamical behavior, presence of thresholds, importance of extreme events or extreme regional stresses caused by global climate change, uncertainty quantification, and the interaction of climate change with the natural and built environments. This paper makes a case for the development of novel algorithms to address these issues, discusses the recent literature, and proposes new directions. An illustrative case study presented here suggests that even relatively simple data mining approaches can provide new scientific insights with high societal impacts.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125619147","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: 75
An FUSP-Tree Maintenance Algorithm for Record Modification 一种记录修改的fusp树维护算法
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.81
Chun-Wei Lin, T. Hong, Wen-Hsiang Lu, Hsin-Yi Chen
{"title":"An FUSP-Tree Maintenance Algorithm for Record Modification","authors":"Chun-Wei Lin, T. Hong, Wen-Hsiang Lu, Hsin-Yi Chen","doi":"10.1109/ICDMW.2008.81","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.81","url":null,"abstract":"There are several algorithms proposed for maintaining the sequential patterns as records are inserted. In addition to record insertion, the pattern maintenance for record modification is also very important in the real-applications. In the past, we have proposed the fast updated sequential pattern tree (called FUSP tree) structure for handling record insertion. In this paper, we attempt to handle the maintenance of sequential patterns for record modification. We do the task by maintaining the FUSP tree and then generate the patterns whenever necessary. An FUSP-tree maintenance algorithm for record modification is thus proposed for reducing the execution time in reconstructing the tree. The proposed approach is expected to achieve a good trade-off between execution time and tree complexity.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127808733","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: 3
Semantic Concept Learning through Massive Internet Video Mining 基于海量网络视频挖掘的语义概念学习
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.114
Peijiang Yuan, Bo Zhang, Jianmin Li
{"title":"Semantic Concept Learning through Massive Internet Video Mining","authors":"Peijiang Yuan, Bo Zhang, Jianmin Li","doi":"10.1109/ICDMW.2008.114","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.114","url":null,"abstract":"Semantic concept learning is one of the most challenging problems in video retrieval. The key barrier for semantic concept learning is lack of annotated training data. Internet videos are different from ordinary videos: massive, rich information, customized, non-uniform format, uneven quality, little descriptive text, only a few shots with limited length etc. Therefore, Internet is a potential repository to provide a reliable source for concept learning. In this paper, we focus on the semantic concept learning through known Internet video sources mining. Starting from the video-sharing websites, an automatical graph model generator for concepts relationship learning based on known ontology such as LSCOM, WordNet and ConceptNet is discussed. An automated source discovery method is addressed which prove to be useful in concept detection from the massive Internet videos. Experimental results prove that the addressed method is effective and efficient in semantic concept detection and learning through massive Internet video mining.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"509 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134228196","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
Service Oriented KDD: A Framework for Grid Data Mining Workflows 面向服务的KDD:网格数据挖掘工作流的框架
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.28
M. Lackovic, D. Talia, Paolo Trunfio
{"title":"Service Oriented KDD: A Framework for Grid Data Mining Workflows","authors":"M. Lackovic, D. Talia, Paolo Trunfio","doi":"10.1109/ICDMW.2008.28","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.28","url":null,"abstract":"Weka4WS is an extension of the Weka toolkit to support remote execution of data mining tasks as grid services. A first version of Weka4WS supporting concurrent execution of multiple data mining tasks on remote grid nodes has been presented in a previous work. In this paper we present a new version supporting also the composition and execution of data mining workflows on a grid. This new version of Weka4WS extends the KnowledgeFlow component of Weka by allowing the data mining tasks of the workflow to run in parallel on different machines, hence reducing the execution time. Besides the performance improvement, the capability of designing data mining applications as workflows allows to define typical patterns and to reuse them in different contexts. In this paper we describe the architecture of the system, the functionalities of the Weka4WS KnowledgeFlow, and some examples of use with their performance.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114512296","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
Mining Allocating Patterns in One-Sum Weighted Items 单和加权项的分配模式挖掘
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.112
Y. Wang, Xinwei Zheng, Frans Coenen, Cindy Y. Li
{"title":"Mining Allocating Patterns in One-Sum Weighted Items","authors":"Y. Wang, Xinwei Zheng, Frans Coenen, Cindy Y. Li","doi":"10.1109/ICDMW.2008.112","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.112","url":null,"abstract":"An association rule (AR) is a common knowledge model in data mining that describes an implicative co-occurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an \"antecedent rArr consequent\" rule. A variant of the AR is the weighted association rule (WAR). With regard to a marketing context, this paper introduces a new knowledge model in data mining - allocating pattern (ALP). An ALP is a special form of WAR, where each rule item is associated with a weighting score between 0 and 1, and the sum of all rule item scores is 1. It can not only indicate the implicative co-occurring relationship between two (disjoint) sets of items in a weighted setting, but also inform the \"allocating\" relationship among rule items. ALPs can be demonstrated to be applicable in marketing and possibly a surprising variety of other areas. We further propose an apriori based algorithm to extract hidden and interesting ALPs from a \"one-sum\" weighted transaction database. The experimental results show the effectiveness of the proposed algorithm.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122243913","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: 3
Incremental Maintenance of Discovered Spatial Colocation Patterns 已发现空间托管模式的增量维护
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.60
Jiangfeng He, Qinming He, Feng Qian, Qi Chen
{"title":"Incremental Maintenance of Discovered Spatial Colocation Patterns","authors":"Jiangfeng He, Qinming He, Feng Qian, Qi Chen","doi":"10.1109/ICDMW.2008.60","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.60","url":null,"abstract":"Unlike the traditional incremental updating problem for discrete data, the appended data to spatial dataset may introduce lots of new relations between the added events and the existing events. Moreover, as the measure in mining of colocation patterns, participation index is complicated to handle compared with simply support counter. Thus, the incremental maintenance of colocation patterns for dynamic spatial dataset becomes a challenging problem. Previous work on traditional incremental maintenance can not tackle it directly. In this study, we introduce the concept of cross in order to reuse the already-known knowledge. Furthermore,we propose an efficient updating algorithm (IMCP) for maintenance of discovered spatial colocation patterns when a set of new spatial data comes.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124686203","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
Detection and Exploration of Outlier Regions in Sensor Data Streams 传感器数据流中离群区域的检测与探索
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.21
Conny Junghans, Michael Gertz
{"title":"Detection and Exploration of Outlier Regions in Sensor Data Streams","authors":"Conny Junghans, Michael Gertz","doi":"10.1109/ICDMW.2008.21","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.21","url":null,"abstract":"Sensor networks play an important role in applications concerned with environmental monitoring, disaster management, and policy making. Effective and flexible techniques are needed to explore unusual environmental phenomena in sensor readings that are continuously streamed to applications. In this paper, we propose a framework that allows to detect outlier sensors and to efficiently construct outlier regions from respective outlier sensors. For this, we utilize the concept of degree-based outliers. Compared to the traditional binary outlier models (outlier versus non-outlier), this concept allows for a more fine-grained, context sensitive analysis of anomalous sensor readings and in particular the construction of heterogeneous outlier regions. The latter suitably reflect the heterogeneity among outlier sensors and sensor readings that determine the spatial extent of outlier regions. Such regions furthermore allow for useful data exploration tasks. We demonstrate the effectiveness and utility of our approach using real world and synthetic sensor data streams.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127205742","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: 22
Distributed Data Mining Models as Services on the Grid 分布式数据挖掘模型作为网格上的服务
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.29
Eugenio Cesario, D. Talia
{"title":"Distributed Data Mining Models as Services on the Grid","authors":"Eugenio Cesario, D. Talia","doi":"10.1109/ICDMW.2008.29","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.29","url":null,"abstract":"This paper describes how distributed data mining models, such as collective learning, ensemble learning, and meta-learning models, can be implemented as WSRF mining services by exploiting the Grid infrastructure. Our goal is to design a general distributed architectural model that can be exploited for different distributed mining algorithms deployed as Grid services for the analysis of dispersed data sources. In order to validate our approach, we present also the implementation of two clustering algorithms on such architecture, and evaluate their performance.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116842024","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: 16
Using Contextual Information to Decrease the Cost of Incorrect Predictions in On-line Customer Behavior Modeling 利用上下文信息降低在线顾客行为建模中错误预测的成本
2008 IEEE International Conference on Data Mining Workshops Pub Date : 2008-12-15 DOI: 10.1109/ICDMW.2008.115
M. Gorgoglione, C. Palmisano, S. Lombardi
{"title":"Using Contextual Information to Decrease the Cost of Incorrect Predictions in On-line Customer Behavior Modeling","authors":"M. Gorgoglione, C. Palmisano, S. Lombardi","doi":"10.1109/ICDMW.2008.115","DOIUrl":"https://doi.org/10.1109/ICDMW.2008.115","url":null,"abstract":"The performance of user profiling models depends on both the predictive accuracy and the cost of incorrect predictions. In this paper we study whether including contextual information leads to a decrease in the misclassification cost. Several experimental analyses were done by varying the cost ratio, the market granularity and the granularity of context. The experimental results show that context leads to a decrease in the misclassification cost under particular conditions. These findings have significant implications for companies that have to decide whether to gather contextual information and make it actionable: how deep it should be and which unit of analysis to consider in market research.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130738916","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: 0
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