The Experts below are selected from a list of 1681041 Experts worldwide ranked by ideXlab platform
Toru Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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COMPSAC - Data Mining Technique Effectiveness for Improving Software Productivity and Quality
2012 IEEE 36th Annual Computer Software and Applications Conference, 2012Co-Authors: Toru KobayashiAbstract:I am engaged in Research and Development for improving software productivity and quality. Currently, I am interested in applying the data mining Techniques to software development projects. Up to now, I examined two use cases which were applied the data mining Techniques to real software development projects. I introduce the results of these specific examples and explain the effectiveness of data mining Techniques.
Osamu Konishi - One of the best experts on this subject based on the ideXlab platform.
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KES (3) - Distributed web integration with multiagent data mining
Lecture Notes in Computer Science, 2005Co-Authors: Ayahiko Niimi, Hitomi Noji, Osamu KonishiAbstract:We proposed a Technique for using multiagent technology in data mining intended for two or more text databases. In this paper, we discuss data mining method based on text (text mining), but our proposed method is not a method of specializing in text mining. First, we proposed data mining Technique using multiagent technology. The proposed Technique is applied to document databases, and discuss its results. In this paper, proposed data mining using multiagent was applied to information integration system on Web, and the effectiveness was verified. In the proposed method, the part of the database access agent was changed to the Web access agent. Also, mining agent was changed to the information extraction agent from the HTML file.
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KES - Extension of Multiagent Data Mining for Distributed Databases
Lecture Notes in Computer Science, 2004Co-Authors: Ayahiko Niimi, Osamu KonishiAbstract:We proposed a Technique for using multiagent technology in data mining intended for two or more text databases. In this paper, we discuss data mining method based on text (text mining), but our proposed method is not a method of specializing in text mining. First, we proposed data mining Technique using multiagent technology. The proposed Technique is applied to document databases, and discuss its results. Next, we extend the proposed Technique with Stem algorithm, English morphological analysis, changed development language, adding the experiment data, and adding data mining algorithm.
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KES - Data Mining for Distributed Databases with Multiagents
Lecture Notes in Computer Science, 2003Co-Authors: Ayahiko Niimi, Osamu KonishiAbstract:We propose a Technique for using multiagent technology in data mining intended for two or more text databases. In this paper, we discuss data mining method based on text (text mining), but our proposed method is not a method of specializing in text mining. First of all, we introduce some typical Techniques as a Technique of data mining to text database. Next, multiagent technology is described. We propose data mining Technique using multiagent technology. The proposed Technique is applied to document databases, and discuss its results.
Zohre Sadat Pourtaghi - One of the best experts on this subject based on the ideXlab platform.
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landslide susceptibility assessment in lianhua county china a comparison between a random forest data mining Technique and bivariate and multivariate statistical models
Geomorphology, 2016Co-Authors: Haoyuan Hong, Hamid Reza Pourghasemi, Zohre Sadat PourtaghiAbstract:Abstract Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining Technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua County, China. Thus, the resultant susceptibility maps will be useful for land use planning and hazard mitigation aims.
Ayahiko Niimi - One of the best experts on this subject based on the ideXlab platform.
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KES (3) - Distributed web integration with multiagent data mining
Lecture Notes in Computer Science, 2005Co-Authors: Ayahiko Niimi, Hitomi Noji, Osamu KonishiAbstract:We proposed a Technique for using multiagent technology in data mining intended for two or more text databases. In this paper, we discuss data mining method based on text (text mining), but our proposed method is not a method of specializing in text mining. First, we proposed data mining Technique using multiagent technology. The proposed Technique is applied to document databases, and discuss its results. In this paper, proposed data mining using multiagent was applied to information integration system on Web, and the effectiveness was verified. In the proposed method, the part of the database access agent was changed to the Web access agent. Also, mining agent was changed to the information extraction agent from the HTML file.
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KES - Extension of Multiagent Data Mining for Distributed Databases
Lecture Notes in Computer Science, 2004Co-Authors: Ayahiko Niimi, Osamu KonishiAbstract:We proposed a Technique for using multiagent technology in data mining intended for two or more text databases. In this paper, we discuss data mining method based on text (text mining), but our proposed method is not a method of specializing in text mining. First, we proposed data mining Technique using multiagent technology. The proposed Technique is applied to document databases, and discuss its results. Next, we extend the proposed Technique with Stem algorithm, English morphological analysis, changed development language, adding the experiment data, and adding data mining algorithm.
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KES - Data Mining for Distributed Databases with Multiagents
Lecture Notes in Computer Science, 2003Co-Authors: Ayahiko Niimi, Osamu KonishiAbstract:We propose a Technique for using multiagent technology in data mining intended for two or more text databases. In this paper, we discuss data mining method based on text (text mining), but our proposed method is not a method of specializing in text mining. First of all, we introduce some typical Techniques as a Technique of data mining to text database. Next, multiagent technology is described. We propose data mining Technique using multiagent technology. The proposed Technique is applied to document databases, and discuss its results.
Rosario Girardi - One of the best experts on this subject based on the ideXlab platform.
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A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text
Intelligent Information Management, 2011Co-Authors: Ivo Serra, Rosario GirardiAbstract:Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) Techniques to identify good candidates of non-taxonomic relationships and a data mining Technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.
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SOCO - Extracting Non-taxonomic Relationships of Ontologies from Texts
Soft Computing, 2011Co-Authors: Ivo Serra, Rosario GirardiAbstract:Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semi-automatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semi-automatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) Techniques to identify good candidates of non-taxonomic relationships and a data mining Technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.
