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Aggregated K Means Clustering and Decision Tree Algorithm for Spirometry Data

K. Rohini, G. Suseendran
2016 Indian Journal of Science and Technology  
In this research work, a combination of k-means clustering algorithm and Decision tree algorithm was developed.  ...  Applications/Improvement: Spirometry data which is used to predict the lung capacity using Aggregated K-means and Decision tree algorithm.  ...  Algorithm for K Means Clustering Decision Tree Algorithm Decision trees are combined of computational and mathematical techniques to aid the representation, generalization and categorization of a given  ... 
doi:10.17485/ijst/2016/v9i44/103107 fatcat:njcgsiqg3vhkvh6nwduoz2bdmq

Eureka!: A Tool for Interactive Knowledge Discovery [chapter]

Giuseppe Manco, Clara Pizzuti, Domenico Talia
2002 Lecture Notes in Computer Science  
The accuracy of clustering results can be validated by using a decision tree classifier, included in the mining tool.  ...  The tool combines a visual clustering method, to hypothesize meaningful structures in the data, and a classification machine learning algorithm, to validate the hypothesized structures.  ...  More importantly, a low-error decision-tree classifier provides a set of rules (directly obtained from the classification tree), that can reveal extremely useful to give an interpretation of each cluster  ... 
doi:10.1007/3-540-46146-9_38 fatcat:ubsuhrn7qfghvga4kwbscygzvy

A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction

Mrinal Pandey, Vivek Kumar Sharma
2013 International Journal of Computer Applications  
In this paper four different decision tree algorithms J48, NBtree, Reptree and Simple cart were compared and J48 decision tree algorithm is found to be the best suitable algorithm for model construction  ...  Here some significant factors have been considered while constructing the decision tree for classifying students according to their attributes (grades).  ...  The process starts from data collection and data preprocessing followed by classification model construction and ends with model evaluation and interpretations.  ... 
doi:10.5120/9985-4822 fatcat:urvdata6yfb2hhizf663tll7wi

Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study

Oleg Metsker, Kirill Magoev, Alexey Yakovlev, Stanislav Yanishevskiy, Georgy Kopanitsa, Sergey Kovalchuk, Valeria V Krzhizhanovskaya
2020 BMC Medical Informatics and Decision Making  
Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy.  ...  For the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of  ...  Ethics approval and consent to participate The local ethics committee of ITMO university approved the study.  ... 
doi:10.1186/s12911-020-01215-w pmid:32831065 pmcid:PMC7444272 fatcat:vzrujd6tljhrrpjm2jd6jlb4fu

An Approach to Determine Non-filer's of Property Tax using Clustering and Classification

Kanchan Jha, Divakar Singh, Diwakar Chaudhary
2015 International Journal of Computer Applications  
In this research, we are using clustering and classification methods to mine the data by using hybrid algorithm. We use K-MEANS algorithm from clustering and CART algorithm from decision tree.  ...  Clustering method will use for make the clusters of similar groups to extract the easily features or properties and decision tree method will use for choose to decide the optimal decision to extract the  ...  After collecting the data we did preprocessing to get the valid and pure data, when data is preprocessed we apply clustering algorithm and get the n number of cluster, when data is divided in different  ... 
doi:10.5120/19582-1330 fatcat:nfeqjrwwczavhftdb4iiq5dvfu

Spatial Clustering and Analysis on Hepatitis C Virus Infections in Egypt

Rania Fathi, Ammar Mohammed, Hesham Hefny
2018 International Journal of Data Mining & Knowledge Management Process  
Spatial data analysis and clustering detection is a vital process in HCV monitoring to discover the area of high risk and to help involved decision makers to draw hypotheses about the cause of disease.  ...  One way that can help in identification of areas with highest diseases is to give a detailed knowledge about the geographical distribution of HCV in Egypt. To achieve that goal,  ...  Nazmun and Ferdous in their research [19] tried to predict HCV in the earlier stages using different Decision Tree techniques J48, LMT, Random Forest, Random tree, REPTree, Decision Stump, and Hoeffding  ... 
doi:10.5121/ijdkp.2018.8501 fatcat:dpjsjz5ifvhdthvnrk75xm3r5u


Darshan Barapatre, Vijayalakshmi A
2017 Asian Journal of Pharmaceutical and Clinical Research  
Hence, the idea is to build a tool which will contain all the required data preparation techniques to make data well-structured by providing greater flexibility and easy to use UI.  ...  According to interviews and experts, data scientists spend 50-80% of the valuable time in the mundane task of collecting and preparing structured or unstructured data, before it can be explored for useful  ...  The real world data are unstructured and extremely complicated to interpret without data preprocessing [1] .  ... 
doi:10.22159/ajpcr.2017.v10s1.20526 fatcat:mv4wr76id5hp7hzzywyhirjtlq

Data Mining in Disease Prediction

Archana Thakur
2022 International Journal of Advanced Research in Computer Science  
Some of the data mining methods include classification, clustering, prediction and association rule mining. In the present work data mining is used for disease prediction.  ...  The disease related data can be processed using data mining techniques to predict various diseases.  ...  This tool holds up a large number of the Classification and Regression Trees (CART), Decision Trees, Association rule mining method, Clustering algorithms and several features are provided for data preprocessing  ... 
doi:10.26483/ijarcs.v13i3.6832 fatcat:zyfmr24eyvgepdziqkiec4gnz4

Machine Learning Method for Pancreatic Cancer Detection using Naïve Bayes and Decision Tree Algorithm

The tumor present in the image will be detected with the help of morphological process and multi clustering model. After Segmentation the image will be divided into various regions.  ...  The paper aims to detect pancreatic cancer with the help of machine learning.  ...  Decision Tree Algorithm: The nodes of the decision tree gets the input in the form of row. The training data set is received by the root node.  ... 
doi:10.35940/ijitee.g5813.059720 fatcat:hczg4iim4jhqrhyoivacsueo7e

A Comparative Study of Engineering Students Pedagogical Progress

Khalid Mahboob, Syed Abbas, Danish Ur, Fayyaz Ali
2018 International Journal of Advanced Computer Science and Applications  
In this research, the classification techniques by k-nearest neighbor, Naïve Bayes and decision trees are applied to evaluate different engineering technologies student's performance and also there are  ...  There are several data mining techniques to apply on education in order to build constructive educational strategies and solutions.  ...  DATA PREPROCESSING AND METHODOLOGY In the field of data mining, data preprocessing is a crucial step to deal with incomplete, noisy and inconsistent data [19] .  ... 
doi:10.14569/ijacsa.2018.090646 fatcat:4qzql7ceqnbfzd24wrq3tojzjq

PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets

Dvir Netanely, Neta Stern, Itay Laufer, Ron Shamir
2019 BMC Bioinformatics  
The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a  ...  array of software tools to be used for the various steps of the analysis.  ...  Declarations The results published here are based upon data generated by The Cancer Genome Atlas managed by the NCI and NHGRI. Information about TCGA can be found at  ... 
doi:10.1186/s12859-019-3142-5 pmid:31878868 pmcid:PMC6933892 fatcat:vchb4qloofgxhagaslyuwbu3hu

A novel decision support system for the interpretation of remote sensing big data

Wadii Boulila, Imed Riadh Farah, Amir Hussain
2017 Earth Science Informatics  
The performance of the proposed system has been demonstrated through three models: clustering, decision tree and association rules.  ...  It helps users easily make decisions in many fields related to RS by providing descriptive, predictive and prescriptive analytics.  ...  Preprocessing of RS data is a key step in the whole processing and interpreting chain [15] . Thus, making decision with RS data cannot use currently conventional DSS tools.  ... 
doi:10.1007/s12145-017-0313-7 fatcat:g57ry2jl7nbbfoqvi332x2poby

Discretization of continuous features in clinical datasets

D. M. Maslove, T. Podchiyska, H. J. Lowe
2013 JAMIA Journal of the American Medical Informatics Association  
TP contributed to the study design, acquisition of data, analysis and interpretation of data, and revising the article.  ...  Contributors DMM contributed to the study design, analysis and interpretation of data, drafting the article, and revising the article.  ...  proposed. 41 Our results help to inform this decision, but may not be applicable to other datasets with different distributions and properties.  ... 
doi:10.1136/amiajnl-2012-000929 pmid:23059731 pmcid:PMC3628044 fatcat:chcaqg76onaepj6ux4ec5wvk4i

Data Mining Techniques for Identification and Classification of Various Diseases in Plants

Data Mining Techniques applied on plants that it helps in segmentation and classification of diseased plants, it avoids Oral Inspection and helps to increase in crop productivity.  ...  This paper specify about data mining techniques for the preprocessing and classification of various disease in plants.  ...  Umair Ayub [5] stated analysis of different data mining classifiers on different feature sets to predict the grass grub damages .The classifiers used are Random Tree, Random forest ,Decision Tree  ... 
doi:10.35940/ijitee.b1110.1292s19 fatcat:lqtfwjlw5besjadoozrzio3yh4

IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees

Adrien Bibal, Antoine Clarinval, Bruno Dumas, Benoît Frénay
2021 Array  
We also acknowledge our two colleagues, Laurent Evrard and Gonzague Yernaux, who kindly agreed to take part in the preliminary evaluation.  ...  Acknowledgements The authors want to thank the participants of the experiment for their time, as well as for the interesting discussions on the pipeline and its implementation.  ...  Furthermore, decision trees stay interpretable even in the case where the original data are high-dimensional (as opposed to, e.g., linear models), as the decisions in the trees consider features one by  ... 
doi:10.1016/j.array.2021.100080 fatcat:3dabozyqsjcwbcesvkyr64xqhe
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