2007 IEEE Symposium on Computational Intelligence and Data Mining最新文献

Algorithms for Fast Large Scale Data Mining Using Logistic Regression 基于逻辑回归的快速大规模数据挖掘算法

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368867

Omid Rouhani-Kalleh

引用次数: 4

Designing a Fuzzy Rule Based System to Estimate Depth of Anesthesia 基于模糊规则的麻醉深度估计系统设计

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368942

V. Esmaeili, A. Assareh, M. Shamsollahi, M. Moradi, N. Arefian

引用次数: 8

A Quantitative Method for Analyzing Scan Path Data Obtained by Eye Tracker 眼动仪扫描路径数据的定量分析方法

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368885

H. Takeuchi, Yoshiko Habuchi

引用次数: 28

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368875

A. Vazifedoost, F. Oroumchian, M. Rahgozar

引用次数: 4

Design of Multi-Singing Karaoke System and its Application in Customer Finance-aided Service on Internet 多唱卡拉ok系统的设计及其在互联网客户金融辅助服务中的应用

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368953

Jian-Hong Wang, Shih-Chuan Feng, Jen-Yi Pan

{"title":"Design of Multi-Singing Karaoke System and its Application in Customer Finance-aided Service on Internet","authors":"Jian-Hong Wang, Shih-Chuan Feng, Jen-Yi Pan","doi":"10.1109/CIDM.2007.368953","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368953","url":null,"abstract":"Financial engineering nowadays is heavily depending on the real-time investment decision-support system while providing the suggestions to investors for rebalancing their portfolios. Because of the rapid growth of interactive technologies in Internet, on-line customer finance-aided service (CFAS) is playing an important role to help discussing, exchanging valuable information or enjoying entertainment each other simultaneously based on the platform of multi-singing karaoke system. Today's online singing software allows only one-microphone performance. Duo singers can only take turns and use the same microphone. Also, they cannot hear each other simultaneously. This is one big disadvantage of online multi-singing software, hence brings the problems to the quality of CFAS. Therefore, the main purpose of this study is to create a system that allows several singers to sing in different places simultaneously and hear the other singers' voices at the same time. This system catches and analyzes the singers' signals from remote computer sources through Media Server. Also by network broadcasting, this system transmits the signals from the inviter's end to the invitee's end, which is deeply improving the quality of CFAS","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121543171","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

Identifying Anatomical Phrases in Clinical Reports by Shallow Semantic Parsing Methods 浅语义分析方法识别临床报告中的解剖短语

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368874

Vijayaraghavan Bashyam, R. Taira

{"title":"Identifying Anatomical Phrases in Clinical Reports by Shallow Semantic Parsing Methods","authors":"Vijayaraghavan Bashyam, R. Taira","doi":"10.1109/CIDM.2007.368874","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368874","url":null,"abstract":"Natural language processing (NLP) is being applied for several information extraction tasks in the biomedical domain. The unique nature of clinical information requires the need for developing an NLP system designed specifically for the clinical domain. We describe a method to identify semantically coherent phrases within clinical reports. This is an important step towards full syntactic parsing within a clinical NLP system. We use this semantic phrase chunker to identify anatomical phrases within radiology reports related to the genitourinary domain. A discriminative classifier based on support vector machines was used to classify words into one of five phrase classification categories. Training of the classifier was performed using 1000 hand-tagged sentences from a corpus of genitourinary radiology reports. Features used by the classifier include n-grams, syntactic tags and semantic labels. Evaluation was conducted on a blind test set of 250 sentences from the same domain. The system achieved overall performance scores of 0.87 (precision), 0.91 (recall) and 0.89 (balanced f-score). Anatomical phrase extraction can be rapidly and accurately accomplished","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124539932","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

ADMIRAL: A Data Mining Based Financial Trading System 基于数据挖掘的金融交易系统

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368947

Gil Rachlin, Mark Last, Dima Alberg, A. Kandel

引用次数: 55

Spatial Data Mining for Optimized Selection of Facility Locations in Field-based Services 面向现场服务设施位置优化选择的空间数据挖掘

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368949

A. Zarnani, M. Rahgozar, C. Lucas, F. Taghiyareh

{"title":"Spatial Data Mining for Optimized Selection of Facility Locations in Field-based Services","authors":"A. Zarnani, M. Rahgozar, C. Lucas, F. Taghiyareh","doi":"10.1109/CIDM.2007.368949","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368949","url":null,"abstract":"Spatial data mining has been developed as the effective technique in many applications that involve large amounts of geo-spatial data. Many organizations provide field-based services such as delivery, field-services and emergency to their customers. Considering the geographical distribution of the customer request points, the location of facilities will have noticeable impact on the overall efficiency of the company's operations. The closer the facilities are to the customers, the sooner and cheaper will be the service provision transaction. In this paper, we empirically study the role of spatial clustering methods in such context. We have implemented and tuned some of the main spatial clustering algorithms to discover the best locations for facility establishment. A new spatial clustering algorithm is proposed that does not require the number of facilities as input. The new algorithm will determine the optimal number of facilities along with their locations based on the business context trade-offs. Many experiments are conducted to study the performance of the studied algorithms on real world and synthetic data sets. The results reveal valuable distinctions between the different methods and confirm the higher efficiency of the proposed algorithm.","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134295703","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

Mining Subspace Correlations 挖掘子空间相关性

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368893

R. Harpaz, R. Haralick

{"title":"Mining Subspace Correlations","authors":"R. Harpaz, R. Haralick","doi":"10.1109/CIDM.2007.368893","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368893","url":null,"abstract":"In recent applications of clustering such as gene expression microarray analysis, collaborative filtering, and Web mining, object similarity is no longer measured by physical distance, but rather by the behavior patterns objects manifest or the magnitude of correlations they induce. Current state of the art algorithms aiming at this type of clustering typically postulate specific cluster models that are able to capture only specific behavior patterns or correlations, and omit the possibility that other information carrying patterns or correlations may coexist in the data. We cast the problem of searching for pattern clusters or clusters that induce large correlations in some subset of features into the problem of searching for groups of points embedded in lines. The advantage of this approach is that is allows the clustering of different patterns or correlations simultaneously. It also allows the clustering of patterns and correlations that are overlooked by existing methods. A formal stochastic line cluster model is presented and its connection to correlation is established. Based on this model an algorithm, which uses feature selection to search for line clusters embedded in subspaces of the data is presented","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127795724","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: 17

Exchange Rates Forecasting Using a Hybrid Fuzzy and Neural Network Model 基于模糊和神经网络混合模型的汇率预测

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368952

An-Pin Chen, Hsio-Yi Lin

引用次数: 3