data mining decision support (79 résultats)

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Taschenbuch. Etat : Neu. Neuware -Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database.Books on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch.

Taschenbuch. Etat : 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.

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modern techniques of capturing data have thrown, besides storage, another couple of challenges to the computer scientists, viz. its quick retrieval and efficient processing. Getting the information quickly in today s ever-increasing data deluge is a key priority for the decision maker. This text examines and describes some new structures and techniques in this area. The purpose of this research is to investigate efficient techniques including data structures, algorithms and their implementations for decision support applications in data warehousing and data mining. The specific techniques proposed include a new efficient indexing structure for approximate query processing, a parallel algorithm for mining frequent patterns, and the mining of value-based itemsets by finding optimal solutions under resource constraints. The effectiveness of each technique has been evaluated using typical test data sets. Written both for computing and information systems researchers, this text is aimed at advanced researchers, particularly, in the area of data warehousing and data mining and, in general, for the database professionals who are keen to know about efficient data organisation.

Hardcover. Etat : New. HardCover. Pub Date: 2018-11-01 Pages: 179 Language: Chinese Publisher: Tongji University Press Data Mining Modeling and Its Application in Electric Power Decision Support Research Tongji Doctoral Discussion Series mainly contains seven parts. respectively Introduction. time series data reduction modeling and application. new distance measurement model and sudden change in power price forecast. cloud .