2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献

Automatic relevance source determination in human brain tumors using Bayesian NMF 基于贝叶斯NMF的人脑肿瘤相关源自动确定

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-30 DOI: 10.1109/CIDM.2014.7008654

S. Ortega-Martorell, I. Olier, M. Julià-Sapé, C. Arús, P. Lisboa

{"title":"Automatic relevance source determination in human brain tumors using Bayesian NMF","authors":"S. Ortega-Martorell, I. Olier, M. Julià-Sapé, C. Arús, P. Lisboa","doi":"10.1109/CIDM.2014.7008654","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008654","url":null,"abstract":"The clinical management of brain tumors is very sensitive; thus, their non-invasive characterization is often preferred. Non-negative Matrix Factorization techniques have been successfully applied in the context of neuro-oncology to extract the underlying source signals that explain different tissue tumor types, for which knowing the number of sources to calculate was always required. In the current study we estimate the number of relevant sources for a set of discrimination problems involving brain tumors and normal brain. For this, we propose to start by calculating a high number of sources using Bayesian NMF and automatically discarding the irrelevant ones during the iterative process of matrices decomposition, hence obtaining a reduced range of interpretable solutions. The real data used in this study come from a widely tested human brain tumor database. Simulated data that resembled the real data was also generated to validate the hypothesis against ground truth. The results obtained suggest that the proposed approach is able to provide a small range of meaningful solutions to the problem of source extraction in human brain tumors.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056418","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: 2

Optimization of relational database usage involving Big Data a model architecture for Big Data applications 涉及大数据的关系数据库使用优化——大数据应用的模型架构

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008703

Erin-Elizabeth A. Durham, Andrew Rosen, R. Harrison

引用次数: 3

A sparsity-based training algorithm for Least Squares SVM 基于稀疏的最小二乘支持向量机训练算法

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008688

Jie Yang, Jun Ma

引用次数: 5

Tibetan-Chinese cross language named entity extraction based on comparable corpus and naturally annotated resources 基于可比语料库和自然标注资源的藏汉交叉语言命名实体抽取

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008680

Yuan Sun, W. Guo, Xiaobing Zhao

引用次数: 0

Quantum clustering — A novel method for text analysis 量子聚类—一种新的文本分析方法

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008143

Ding Liu, Minghu Jiang, Xiaofang Yang

引用次数: 1

Clustering data over time using kernel spectral clustering with memory 随着时间的推移，使用内存核谱聚类对数据进行聚类

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008141

R. Langone, Raghvendra Mall, J. Suykens

引用次数: 10

Matching social network biometrics using geo-analytical behavioral modeling 利用地理分析行为模型匹配社会网络生物特征

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008699

M. Rahmes, K. Fox, J. Delay, Gran Roe

{"title":"Matching social network biometrics using geo-analytical behavioral modeling","authors":"M. Rahmes, K. Fox, J. Delay, Gran Roe","doi":"10.1109/CIDM.2014.7008699","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008699","url":null,"abstract":"Social patterns and graphical representation of geospatial activity is important for describing a person's typical behavior. We discuss a framework using social media and GPS smart phone to track an individual and establish normal activity with a network biometric. An individual's daily routine may include visiting many locations - home, work, shopping, entertainment and other destinations. All of these activities pose a routine or status quo of expected behavior. What has always been difficult, however, is predicting a change to the status quo, or predicting unusual behavior. We propose taking the knowledge of location information over a relatively long period of time and marrying that with modern analytical capabilities. The result is a biometric that can be fused and correlated with another's behavioral biometric to determine relationships. Our solution is based on the analytical environment to support the ingestion of many data sources and the integration of analytical algorithms such as feature extraction, crowd source analysis, open source data mining, trends, pattern analysis and linear game theory optimization. Our framework consists of a hierarchy of data, space, time, and knowledge entities. We exploit such statistics to predict behavior or activity based on past observations. We use multivariate mutual information as a measure to compare behavioral biometrics.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132829842","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: 4

Generalized information theoretic cluster validity indices for soft clusterings 广义信息理论的软聚类有效性指标

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008144

Yang Lei, J. Bezdek, Jeffrey Chan, X. Nguyen, Simone Romano, J. Bailey

引用次数: 13

Ontology learning with complex data type for Web service clustering 面向Web服务集群的复杂数据类型本体学习

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008658

B. Kumara, Incheon Paik, K. Koswatte, Wuhui Chen

{"title":"Ontology learning with complex data type for Web service clustering","authors":"B. Kumara, Incheon Paik, K. Koswatte, Wuhui Chen","doi":"10.1109/CIDM.2014.7008658","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008658","url":null,"abstract":"Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. Further, current clustering approaches are considered only have simple data types in services' input and output. However, services that published on the web have input/ output parameter of complex data type. In this research, we propose clustering approach that considers the simple type as well as complex data type in measuring the service similarity. We use hybrid term similarity method which we proposed in our previous work to measure the similarity. We capture the semantic pattern exist in complex data types and simple data types to improve the ontology learning method. Experimental results show our clustering approach which uses complex data types in measuring similarity works efficiently.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129889511","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

New bilinear formulation to semi-supervised classification based on Kernel Spectral Clustering 基于核谱聚类的半监督分类新双线性公式

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008146

V. Jumutc, J. Suykens

引用次数: 0