Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important researc...
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Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh's individual characteristics and spatial context. The classifier is generated using the c4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.
Diabetes is a disease in which the body's ability to produce or respond to the hormone insulin is impaired, resulting in abnormal metabolism of carbohydrates and elevated levels of glucose in the blood and urine. ...
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ISBN:
(纸本)9781509062843
Diabetes is a disease in which the body's ability to produce or respond to the hormone insulin is impaired, resulting in abnormal metabolism of carbohydrates and elevated levels of glucose in the blood and urine. It can be suffered by everyone and until now there is no cure for it. A hospital readmission is an episode when a patient who had been discharged from a hospital is admitted again within a specified time interval. Readmission rates have increasingly been used as a quality benchmark for health systems. In this research, c.45 algorithm is used to determine hospital readmission rate of diabetes patient. Dataset used in this study is taken from UcI Machine Learning Repository, which contain diabetic patient data from 130 hospitals in United States for 10 years (1999-2008). Several experiments are done to get the best result, and the best result is 74.5% for accuracy. This result is obtained by doing several preprocess data i.e. filling all missing value, using numeric and nominal attribute type, and by not including several attributes.
The Decision Tree technology, which is the main technology of the Data Mining classification and forecast, is the classifying rule that infers the Decision Tree manifestation through group of out-of-orders, the non-ru...
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ISBN:
(纸本)9783037853559
The Decision Tree technology, which is the main technology of the Data Mining classification and forecast, is the classifying rule that infers the Decision Tree manifestation through group of out-of-orders, the non-rule examples. Based on the research background of The Decision Tree's concept, the c4.5 algorithm and the construction of The Decision Tree, the using of c4.5 Decision Tree algorithm was applied to result analysis of students' score for the purpose of improving the teaching quality.
The development needs the internet and cable television entertainment increase per year that affect popping up various multimedia service provider company which is offered a lot of services to win the market. This mak...
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ISBN:
(纸本)9781509016488
The development needs the internet and cable television entertainment increase per year that affect popping up various multimedia service provider company which is offered a lot of services to win the market. This makes customer has a lot of company choices and makes customer to be more demanded and move easily from a provider to other provider, where company knows that keep customer has the cost that is lower than the cost to get new customer. So, it's important for company to know customer loyalty and company can also project the income as reference in company development planning. company needs to has accurate model, so researcher uses k-means segmentation and c4.5classification algorithm, which can be seen that the model has accuracy 79.33% and Area Under curve (AUc) 0.831. This research contribution is the use of related data using customer potential segmentation based on Recency Frequency Monetary (RFM) model, so can increase accuracy percentage in customer loyalty classification research.
On the base of introducing the Decision Tree, this article lists two main algorithms of Decision Tree. After describing the principle of briefly, it reveals the essential idea and the computing process of c4.5 Algorit...
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ISBN:
(纸本)9781538645093
On the base of introducing the Decision Tree, this article lists two main algorithms of Decision Tree. After describing the principle of briefly, it reveals the essential idea and the computing process of c4.5 algorithm in detail. As constructing a Decision Tree on a group of instance data with the two algorithms, we get the principle of classification about the data. By comparing the two algorithms during the process of constructing the tree, we analysis the advantages and disadvantages of them finally. With the conclusion of the applied scope of each algorithm, it provides a theoretical basis for choosing an appropriate method in practical application.
By analyzing the shortcomings of c4.5 decision tree algorithm, this paper combines MapReduce parallel model in Hadoop platform with c4.5 decision tree algorithm, so that c4.5 algorithmcan be executed in parallel, so ...
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By analyzing the shortcomings of c4.5 decision tree algorithm, this paper combines MapReduce parallel model in Hadoop platform with c4.5 decision tree algorithm, so that c4.5 algorithmcan be executed in parallel, so as to improve the efficiency of the algorithm. The improved c4.5 algorithm implementation process is given and the process is analyzed. Finally, the c4.5 algorithm improved experimental environment configuration is explained. (c) 2021 The Authors. Published by Elsevier B.V.
The paper introduces a balanced coefficient to improve the veracity of c4.5 algorithm. It can be fixed by decision maker according to priori intellectual and domain intellectual. It harmonized the information gain-rat...
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ISBN:
(纸本)9780769538655
The paper introduces a balanced coefficient to improve the veracity of c4.5 algorithm. It can be fixed by decision maker according to priori intellectual and domain intellectual. It harmonized the information gain-ratio of each attributes artificially in specific environment. The classification is more veracious and rational by the decision tree made from the improved algorithm. And compared the improved algorithm to c4.5 algorithm by analyzing examples, to prove the efficiency of the improved algorithm.
Water quality has a very important role in the success of shrimp farming. Water as a living medium for shrimp, has a direct effect on the health and growth of shrimp. Water quality determines the existence of various ...
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ISBN:
(纸本)9781728107264
Water quality has a very important role in the success of shrimp farming. Water as a living medium for shrimp, has a direct effect on the health and growth of shrimp. Water quality determines the existence of various types of organisms in the pond ecosystem, both for shrimp and other biota. Water quality that is far from the optimal value can cause cultivation failure, while optimal water quality can support shrimp growth. This study aims to build a decision support system to help companies manage water quality to get good shrimp farming results. The attributes used are water height, salinity, DO, pH, temperature, alkalinity and target size attributes of shrimp. The data used were 93 data partitioned into 76 training data and 17 testing data. Based on the classification results using the c4.5 algorithm, accuracy was 76.47%, precision was 72.72% and recall was 88.88%. From this study it was proven that the decision support system with the c4.5 algorithmcan be used to determine the results of vaname shrimp farming.
The background of the research is to analyze data derived from an elucidation of catfish and carp diseases in Kediri, East Java, Indonesia. The research shows that data about fish's disease history have not been u...
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ISBN:
(纸本)9781509017218
The background of the research is to analyze data derived from an elucidation of catfish and carp diseases in Kediri, East Java, Indonesia. The research shows that data about fish's disease history have not been used effectively because it is only be collected. Data about fish's symptom history used by fish trainer only present the number of fish that get disease. Data about fish's history should be also optimized to discover the relationship among fish's disease. Thus, anticipation about disease that always attack fish could be prevented earlier. The research is done to understand the relationship history among fish's disease. Then the accuracy of relationship quality is measured to acquire the quality of data properly so it can be worked to identify fish's disease. Data relationship quality among fish's disease symptoms should be understood to know how is the accuracy of datum classification obtained. A proper method is required to extract information from data obtained. There are many data-mining classification algorithms such as cART, cHAID, Rain Forest, and c4.5. But, the c4.5 algorithm is appropriate for this research used to form decision tree for data quality assessed from accurate performance of some multi-class fish diseases. This research uses 1120 data involving six diseases. The data were obtained from Agriculture Board (fishery subdivision) of Kediri Regency. The result shows that c4.5 algorithm is well to do for both a low and high accuracy class at 55.3 and 88.4 percent.
Study program of Information Systems is one of the existing study programs at Telkom University which has produced many graduates until 2017. However, not all graduates produced successfully completed the study period...
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ISBN:
(纸本)9781538654330
Study program of Information Systems is one of the existing study programs at Telkom University which has produced many graduates until 2017. However, not all graduates produced successfully completed the study period during four years of normal study period in which may cause the decrease of study programs quality and affect the assessment of study program if there is an audit or evaluation so it can affect the achievement level of the study program. To solve the problem can be by making a prediction model of student graduation that can be obtained from data classification process using decision tree with algorithmc4.5 and implement it to the academic data record of existing student so that got two group of student, that is student which predicted pass on time and student predicted to pass late. From the results of the classification of student data can be done an analysis of what factors that can affect the graduation of students who are predicted to pass on time and plan appropriate strategies for groups of students who may not pass on time. The data classification process is done with the help of open source based tools using RapidMiner application. The result of the classification is a prediction model that has an accuracy value of 82.24% and states that the most influential factor in predicting students' graduation is GPA in the second year. The result of the student's graduation classification is expected to be used as the reference base to support the academic planner in making the right decision to the student groups generated so that all students can graduate on time.
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