scholarly journals Research on Spatial and Dynamic Planning Methods for Settlement Buildings Based on Data Mining

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Lin Chen ◽  
Xiaolong Chen ◽  
Hongxin Wang ◽  
Lin Zhu ◽  
Lingyun Lang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Spatial Data ◽  
Residential Buildings ◽  
User Interest ◽  
Grid Pattern ◽  
Complete Subgraph ◽  
Architectural Space ◽  
Id3 Algorithm ◽  
Protection And Renewal

Traditional settlements are widely concerned by academic circles for their unique settlement patterns, exquisite residential buildings, and rich historical and cultural connotations, and their protection and development is an important proposition for rural revitalization. Therefore, from the perspective of big data mining (BDM), this paper explores its application in architectural space and settlement protection of traditional settlements in Hainan and provides new ideas for the protection and renewal of traditional settlements in Hainan. The attribute elements of spatial data of settlement groups are analyzed by the decision tree classification mining method. In order to avoid the multivalued tendency of ID3 algorithm and improve the efficiency of decision tree generation by ID3 algorithm, an improved ID3 algorithm is proposed by introducing user interest and simplifying the calculation process of the algorithm. At the same time, the graph theory recognition method of grid pattern is proposed. Aiming at the intersection graph and direction relation graph of straight line pattern, grid pattern recognition is realized by solving the connectivity, intersection, and subsequent construction of the maximum complete subgraph. Experiments show that the improved ID3 algorithm has better running efficiency than the parallel algorithm based on cooccurrence matrix. The analysis of the architectural space of traditional settlements in Hainan will help us better grasp social activities and provide direction for the protection and renewal of traditional settlements from the perspective of tourists and residents.

scholarly journals Penerapan Algoritma ID3 dalam Prediksi Kebutuhan Pupuk

2021 ◽  
Vol 2 (4) ◽  
pp. 247-253
Author(s):  
Milyani Aritonang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Plant Protection ◽  
Organic Fertilizer ◽  
Urea Fertilizer ◽  
Id3 Algorithm ◽  
Development Unit ◽  
Npk Fertilizer

The need for fertilizer at the Plant Protection Development Unit (UPPT) is uncertain depending on the demand of farmers, therefore it is necessary to predict fertilizer needs. There are five types of fertilizers predicted by the Plant Protection Development Unit (UPPT), including Urea fertilizer, ZA fertilizer, SP-36 fertilizer, NPK fertilizer, and Organic fertilizer, so fertilizer needs can be predicted. In predicting data mining on fertilizer needs using the ID3 algorithm. Where it works is calculating the value of entropy and gain to get the final result in the form of a tree to the decision and rule. Testing is done using the tanagra software. The results of the tests carried out on the tanagra application using the ID3 algorithm are in the form of a decision tree, while in the calculation the results obtained are in the form of a decision tree.

scholarly journals Developed third iterative dichotomizer based on feature decisive values for educational data mining

2020 ◽  
Vol 18 (1) ◽  
pp. 209
Author(s):  
Saja Taha Ahmed ◽  
Rafah Al-Hamdani ◽  
Muayad Sadik Croock
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Decision Tree ◽  
Predictive Analytics ◽  
Educational Data Mining ◽  
Target Class ◽  
Id3 Algorithm ◽  
Feature Weight ◽  
Holdout Validation ◽  
Fold Cross Validation

<p><span>Recently, the decision trees have been adopted among the preeminent utilized classification models. They acquire their fame from their efficiency in predictive analytics, easy to interpret and implicitly perform feature selection. This latter perspective is one of essential significance in Educational Data Mining (EDM), in which selecting the most relevant features has a major impact on classification accuracy enhancement. <br /> The main contribution is to build a new multi-objective decision tree, which can be used for feature selection and classification. The proposed Decisive Decision Tree (DDT) is introduced and constructed based on a decisive feature value as a feature weight related to the target class label. The traditional Iterative Dichotomizer 3 (ID3) algorithm and the proposed DDT are compared using three datasets in terms of some ID3 issues, including logarithmic calculation complexity and multi-values features<em></em>selection. The results indicated that the proposed DDT outperforms the ID3 in the developing time. The accuracy of the classification is improved on the basis of 10-fold cross-validation for all datasets with the highest accuracy achieved by the proposed method is 92% for the student.por dataset and holdout validation for two datasets, i.e. Iraqi and Student-Math. The experiment also shows that the proposed DDT tends to select attributes that are important rather than multi-value. </span></p>

Application of Decision Tree Algorithm in Lumber Hierarchies

2012 ◽  
Vol 466-467 ◽  
pp. 308-313
Author(s):  
Dan Guo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Inductive Reasoning ◽  
Decision Tree Algorithm ◽  
Discrete Function ◽  
Tree Algorithm ◽  
Id3 Algorithm ◽  
Noise Data ◽  
Construction Model

The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decision tree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.

scholarly journals Human Heart Disease Prediction System Using Data Mining Techniques

2019 ◽  
Vol 8 (II) ◽  
Author(s):  
Sidra Javed ◽  
Hamza Javed ◽  
Ayesha Saddique ◽  
Beenish Rafiq
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Heart Diseases ◽  
Heavy Load ◽  
Data Mining Techniques ◽  
Id3 Algorithm ◽  
Wide Range ◽  
Using Data ◽  
Human Heart Disease

— Prediction of heart disease is a big concern now a days because everyone is busy and due to heavy load of work people do not give attention to their health. To diagnose a disease is a big challenge. The issue is to extract data that have some meaningful knowledge. For this purpose, data mining techniques are used to extract meaningful data. Decision Tree and ID3 are used to predict heart diseases. Many researchers and practitioners are familiar with prediction of heart diseases and wide range of techniques is available to predict disease. To address this problem, Decision Tree is used to predict the heart disease. In this study the collected data is pre-processed, Decision Tree algorithm and ID3 were then applied to predict the heart disease.   Index Terms— Decision Tree, ID3 Algorithm, Data Mining, Decision Support System (DSS), knowledge Discovery from Databases (KDD).

scholarly journals C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

2020 ◽  
Vol 27 (3) ◽  
pp. 29-43
Author(s):  
Sihem Oujdi ◽  
Hafida Belbachir ◽  
Faouzi Boufares
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Spatial Data ◽  
Computation Time ◽  
Spatial Relations ◽  
Spatial Data Analysis ◽  
Data Organization ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time.

The Application of Decision Tree Algorithm in the Employment Management System

2014 ◽  
Vol 543-547 ◽  
pp. 1639-1642 ◽  
Author(s):  
Liang Li ◽  
Ying Zheng ◽  
Xiao Hua Sun ◽  
Fu Shun Wang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Effective Means ◽  
University Graduates ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Id3 Algorithm ◽  
Decision Attributes ◽  
C4.5 Algorithm ◽  
Employment Data

According to students' employment problem, employment data mining model of university graduates is presented. The decision tree is very effective means for classification, which is proposed according to the characteristics of employment data and C4.5 algorithm. The C4.5 algorithm is improved from ID3 algorithm that is the core algorithm in the decision tree. The C4.5 algorithm is suitable for its simple construction, high processing speed and easy implementation. The model includes preprocess of the data of employment selection of decision attributes, implementation of mining algorithm, and obtainment of rules from the decision tree. The rules point out which decision attributes decide the classification of employers. Case study shows that the decision tree algorithm applied to employment information data mining, can classify data of employment correctly with simple structure and faster speed, and find some valuable results for analysis and decision. so the proposed algorithm in this paper is effective.

The Optimization Algorithm and Applied in Soil Fertility Evaluation Based on Data Mining

2014 ◽  
Vol 644-650 ◽  
pp. 1737-1740
Author(s):  
Li Ma ◽  
Gui Fen Chen
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Soil Fertility ◽  
Spatial Data ◽  
Rough Sets ◽  
Evaluation Model ◽  
Attribute Reduction ◽  
Spatial Data Mining ◽  
New Ideas

Clustering, rough sets and decision tree theory were applied to the evaluation of soil fertility levels ,and provided new ideas and methods among the spatial data mining and knowledge discovery. In the experiment, the rough sets - decision tree evaluation model establish by 1400 study samples, the accuracy rate is 92% of the test. The results show :model has good generalization ability; the use of rough sets attribute reduction, can remove redundant attributes, can reduce the size of decision tree decision-making model, reduce the decision-making rules and improving the decision-making accuracy, using the combination of rough set and decision tree decision-making method to infer the level of a large number of unknown samples.

scholarly journals Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Li Ju ◽  
Lei Huang ◽  
Sang-Bing Tsai
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Optimization Algorithm ◽  
Nearest Neighbor ◽  
Original Data ◽  
Data Migration ◽  
Travel Patterns ◽  
Online Data ◽  
Migration Model ◽  
Id3 Algorithm

The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a k -nearest neighbor-based ID3 optimization algorithm, which selects values similar to k -nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the k -nearest neighbor-based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher-performance online data migration model for sports competition action data mining.

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