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Sinopsis
Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use
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Acerca del autor
Dr. Parashu Ram Pal, obtained Ph.D. in Computer Science. He is working as a Professor in Department of Information Technology, ABES Engineering College, Ghaziabad, India. He has published three books and more than 40 Research Papers in various International, National Journals & Conferences. He is devoted to Education, Research & Development for more than twenty years and always try to create a proper environment for imparting quality education with the spirit of service to the humanity. He believes in motivating the colleagues and students to achieve excellence in the field of education and research.
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- EditorialGRIN Verlag
- Año de publicación2021
- ISBN 10 3346292320
- ISBN 13 9783346292322
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas140
- Contacto del fabricanteno disponible
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Data Mining Multi-Attribute Decision System. Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi-Attribute Decision Models
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GRIN Verlag Jan 2021, 2021
ISBN 10: 3346292320
ISBN 13: 9783346292322
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms. 140 pp. Englisch. Nº de ref. del artículo: 9783346292322
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Data Mining Multi-Attribute Decision System. Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi-Attribute Decision Models
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms. Nº de ref. del artículo: 9783346292322
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Data Mining Multi-Attribute Decision System. Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi-Attribute Decision Models
Librería: California Books, Miami, FL, Estados Unidos de America
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Data Mining Multi-Attribute Decision System. Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi-Attribute Decision Models
Publicado por
GRIN Verlag, GRIN Verlag Jan 2021, 2021
ISBN 10: 3346292320
ISBN 13: 9783346292322
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms. 140 pp. Englisch. Nº de ref. del artículo: 9783346292322
Cantidad disponible: 2 disponibles
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Data Mining Multi-Attribute Decision System. Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi-Attribute Decision Models
Librería: preigu, Osnabrück, Alemania
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Taschenbuch. Condición: Neu. Data Mining Multi-Attribute Decision System. Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi-Attribute Decision Models | Parashu Ram Pal (u. a.) | Taschenbuch | Englisch | 2021 | GRIN Verlag | EAN 9783346292322 | Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Nº de ref. del artículo: 119554205
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