2015 International Workshop on Data Mining with Industrial Applications (DMIA)最新文献

A Mining Approach to Evaluate Geoportals Usability 一种评估地理门户可用性的挖掘方法

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.22

Esther Hochsztain

{"title":"A Mining Approach to Evaluate Geoportals Usability","authors":"Esther Hochsztain","doi":"10.1109/DMIA.2015.22","DOIUrl":"https://doi.org/10.1109/DMIA.2015.22","url":null,"abstract":"A geoportal is a basic component of a spatial data infrastructure, used for searching, viewing, and downloading spatial data and services. It can be considered as a web application acting as an access point to the shared geographic information, being the place where distributed geographic data and services can be discovered. A geoportal offers the opportunity for different type of organizations to make their data and services accessible for the whole community of internet users and geoportals usability is considered a key concept. From the organizations point of view, usability is related to how the geoportal can support people to perform their tasks effectively and efficiently. From the end-users' point of view, usability concerns to how a geoportal is perceived, in a satisfying manner, to support users tasks. In this paper we present a mining approach to discover patterns related to geoportals usability evaluation. A framework is proposed based on several data sources in order to identify geoportals strengths and weaknesses affecting usability. The requirements emerging and its implications are analyzed. A geoportal evaluation case study is presented, performing web server logs analysis and System Usability Scale (SUS) questionnaire experimental data analysis applying factor analysis and association rules.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134253118","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

Feature Grouping and Selection on High-Dimensional Microarray Data 高维微阵列数据的特征分组与选择

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.18

M. García-Torres, Francisco Gómez-Vela, D. Becerra-Alonso, B. Melián-Batista, Marcos Moreno-Vega

{"title":"Feature Grouping and Selection on High-Dimensional Microarray Data","authors":"M. García-Torres, Francisco Gómez-Vela, D. Becerra-Alonso, B. Melián-Batista, Marcos Moreno-Vega","doi":"10.1109/DMIA.2015.18","DOIUrl":"https://doi.org/10.1109/DMIA.2015.18","url":null,"abstract":"In classification tasks, as the dimensionality increases, the performance of the classifier improves until an optimal number of features is reached. Further increases of the dimensionality without increasing the number of training samples results in a degradation in classifier performance. This fact, called the curse of dimensionality, has become more relevant with the advent of larger datasets and the demands of Knowledge Discovery from Big Data. In this context, feature grouping has become an effective approach to provide additional information about relationships between features. In this work, we propose a greedy strategy, called GreedyPGG, that groups features based on the concept of Markov blankets. To such aim, we introduce the idea of predominant group of features. We also present an adaptation of the Variable Neighborhood Search (VNS) to high-dimensional feature selection that uses the GreedyPGG to reduce the search space. We test the effectiveness of the GreedyPGG on synthetic datasets and the VNS on microarray datasets. We compare VNS with popular and competitive strategies. Results show that GreedyPGG groups correlated features in an efficient way and that VNS is a competitive strategy, capable of finding a small number of features with high predictive power.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127674196","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

Application of Business Intelligence Techniques to Analyze IT Project Management Data 应用商业智能技术分析IT项目管理数据

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.15

A. Tasistro

引用次数: 1

Investigating the Role of Individual Neurons as Outlier Detectors 研究单个神经元作为异常值检测器的作用

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.11

C. López-Vázquez

引用次数: 0

Feature Selection via Approximated Markov Blankets Using the CFS Method 基于CFS方法的近似马尔可夫毛毯特征选择

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.17

Rafael Arias-Michel, M. García-Torres, C. Schaerer, F. Divina

{"title":"Feature Selection via Approximated Markov Blankets Using the CFS Method","authors":"Rafael Arias-Michel, M. García-Torres, C. Schaerer, F. Divina","doi":"10.1109/DMIA.2015.17","DOIUrl":"https://doi.org/10.1109/DMIA.2015.17","url":null,"abstract":"Feature selection has become an important research area in machine learning due to rapid advances in technology. In high-dimensional spaces, the difficulty of classification is intrinsically caused by the existence of irrelevant and redundant features that, in general, degrade the performance of a classifier. Moreover, finding the optimal subset of features becomes intractable even for low-dimensional datasets. In this context, Markov blanket discovery can be used to identify such subset. The approximated Markov blanket (AMb) is an efficient and effective approach to induce Markov blankets from data. However, this approach only considers pairwise comparisons of features. In this paper, we redefine the AMb to consider the interaction among features of a given subset of features. We use the Correlation based Feature Selection (CFS) function to measure such interactions and, as search strategy, the Fast Correlation based Filter (FCBF). The proposal, denoted as FCBFCFS, is compared with the FCBF and tested on synthetic and real-world datasets from the microarray domain. Results show that the inclusion of interactions among features in a subset may led to smaller subsets of features without degrading the classification task.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016513","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

Towards a Data Processing Architecture for the Weather Radar of the INTA Anguil INTA安吉尔气象雷达数据处理体系研究

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.12

M. Diván, Yanina Bellini Saibene, Maria de los Ángeles Martín, María Laura Belmonte, Guillermo Lafuente, J. Caldera

引用次数: 7

Teaching an Learning Business Intelligence: Business Evaluation Last but Not Least 教与学商业智能:商业评估最后但并非最不重要

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.19

Esther Hochsztain, A. Tasistro

引用次数: 0

Predictive Models of Economic Systems Based on Data Mining 基于数据挖掘的经济系统预测模型

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.20

J. Cazal

{"title":"Predictive Models of Economic Systems Based on Data Mining","authors":"J. Cazal","doi":"10.1109/DMIA.2015.20","DOIUrl":"https://doi.org/10.1109/DMIA.2015.20","url":null,"abstract":"Data election to build a representative model able to explain socio-economic phenomena is a challenge within the model construction stage itself. Knowing what data to include within the studies and what to discard is a challenge, and again, at the same time, a great amount of possible factors affecting each variable behavior must be found. In complex phenomena, the number of factors affecting a variable is enormous, and isolating a variable can become a hopeless effort. Besides, there are also factors that are difficultly observable or inherently not observable that must be considered, those ones known as errors or perturbations in a relation that have influence in the constructed model outputs. Techniques applied in data mining can give support to the studies in the moment of analyzing the socio-economic phenomena and demonstrate results obtained through a scientific and reliable way. Data mining is proposed as a valid option in the study of indicators contrasting the traditional methodology (econometrics). An experiment was conducted to contrast two cultures in the use of statistical modeling. One assumes that the data are generated by stochastic GIVEN data model (Data Modeling Culture). The other one uses algorithmic models and treats the data as unknown mechanism (Algorithmic Modeling Culture).","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133601760","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

Data Mining Applications in Entrepreneurship Analysis 数据挖掘在创业分析中的应用

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.21

Esther Hochsztain, A. Tasistro, M. Messina

引用次数: 4

An Integrated Strategy Based in Processes, Requirements, Measurement and Evaluation for the Formalization of Necessities in Data Warehouse Projects 基于过程、需求、度量和评价的数据仓库项目需求形式化集成策略

2015 International Workshop on Data Mining with Industrial Applications (DMIA) Pub Date : 2015-09-01 DOI: 10.1109/DMIA.2015.13

Avalos Veronica Nathali, Diván Mario José

{"title":"An Integrated Strategy Based in Processes, Requirements, Measurement and Evaluation for the Formalization of Necessities in Data Warehouse Projects","authors":"Avalos Veronica Nathali, Diván Mario José","doi":"10.1109/DMIA.2015.13","DOIUrl":"https://doi.org/10.1109/DMIA.2015.13","url":null,"abstract":"In this work we proposes an Integrated Strategy based in Processes, Requirements, Measurement and Evaluation, whose objective is to identify and maintain a traceability of such requirements at early stages in data warehouse projects. Our strategy starts with the process formalization using SPEM to improve its communicability and extensibility. From the process formalization, we continue with the definition of the measurement and evaluation (M&E) project to quantify the behavior of each process and its necessities. This enables progress in the project scoping, the early identification of its risks, and at the same time establishes a traceability mechanism between the decisions and the artifacts that may generate throughout its life cycle. This represents an important compliment regarding life cycles as proposed by Kimball, in which the requirement phase is not formalized and there is no strategy to clearly define the aspects to quantify and/or analyze to the effects of supporting a decision making process. Finally, an example of the strategy application for one process of the Ministry of Education of Ecuador is shown.","PeriodicalId":387758,"journal":{"name":"2015 International Workshop on Data Mining with Industrial Applications (DMIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133712530","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