scholarly journals DEVELOPING A PARALLEL CLASSIFIER FOR MINING IN BIG DATA SETS

2021 ◽  
Vol 22 (2) ◽  
pp. 119-134
Author(s):  
Ahad Shamseen ◽  
Morteza Mohammadi Zanjireh ◽  
Mahdi Bahaghighat ◽  
Qin Xin
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Main Memory ◽  
Experimental Results ◽  
Primary Data ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Vast Amount ◽  
Tree Classifier

Data mining is the extraction of information and its roles from a vast amount of data. This topic is one of the most important topics these days. Nowadays, massive amounts of data are generated and stored each day. This data has useful information in different fields that attract programmers’ and engineers’ attention. One of the primary data mining classifying algorithms is the decision tree. Decision tree techniques have several advantages but also present drawbacks. One of its main drawbacks is its need to reside its data in the main memory. SPRINT is one of the decision tree builder classifiers that has proposed a fix for this problem. In this paper, our research developed a new parallel decision tree classifier by working on SPRINT results. Our experimental results show considerable improvements in terms of the runtime and memory requirements compared to the SPRINT classifier. Our proposed classifier algorithm could be implemented in serial and parallel environments and can deal with big data. ABSTRAK: Perlombongan data adalah pengekstrakan maklumat dan peranannya dari sejumlah besar data. Topik ini adalah salah satu topik yang paling penting pada masa ini. Pada masa ini, data yang banyak dihasilkan dan disimpan setiap hari. Data ini mempunyai maklumat berguna dalam pelbagai bidang yang menarik perhatian pengaturcara dan jurutera. Salah satu algoritma pengkelasan perlombongan data utama adalah pokok keputusan. Teknik pokok keputusan mempunyai beberapa kelebihan tetapi kekurangan. Salah satu kelemahan utamanya adalah keperluan menyimpan datanya dalam memori utama. SPRINT adalah salah satu pengelasan pembangun pokok keputusan yang telah mengemukakan untuk masalah ini. Dalam makalah ini, penyelidikan kami sedang mengembangkan pengkelasan pokok keputusan selari baru dengan mengusahakan hasil SPRINT. Hasil percubaan kami menunjukkan peningkatan yang besar dari segi jangka masa dan keperluan memori berbanding dengan pengelasan SPRINT. Algoritma pengklasifikasi yang dicadangkan kami dapat dilaksanakan dalam persekitaran bersiri dan selari dan dapat menangani data besar.

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

CLUSTERING‐BASED DECISION TREE CLASSIFIER CONSTRUCTION / KLASTERIŲ SPRENDIMŲ MEDŽIU PAGRĮSTAS STATYBOS KLASIFIKATORIUS

2010 ◽  
Vol 16 (4) ◽  
pp. 765-781 ◽  
Author(s):  
Inese Polaka ◽  
Arkady Borisov
Keyword(s):  
Cluster Analysis ◽  
Data Structure ◽  
Decision Tree ◽  
Data Sets ◽  
Structure Investigation ◽  
Decision Tree Classifier ◽  
Tree Classifier

This article studies data structure investigation possibilities using cluster analysis. Density structures within classes are explored to implement class decomposition in order to enhance performance of decision tree classifiers. Classes are decomposed using cluster analysis and cluster merge evaluation using decision tree classifiers. Then impact of class decomposition is shown on C4.5 and CART classifiers. The main focus is on experiments carried out with real‐valued data sets. The experiments are described in a step‐by‐step manner to illustrate the patterns discovered which affect previously proposed patterns in class decomposition methodology. Santrauka Straipsnyje nagrinėjamos duomenų struktūros tyrimo galimybės naudojant klasterinė analizė. Tiriamos įvairaus tankio klasės ir siekiama tokio klasės išskaidymo laipsnio, kad padidetų sprendimų medžio klasifikatorių veiksmingumas. Klasės suskaidomos klasterinės analizės ir klasterių sujungimo metodais, naudojant sprendimų medžio klasifikatorius. Tada klasės skilimo poveikis parodomas C4.5 ir CART klasifikatoriuose. Pagrindinis dėmesys skiriamas bandymams, atliktiems su realiu duomenų rinkiniais. Eksperimentai aprašomi žingsnis po žingsnio, iliustruojant atrastus modelius, kurie veikia anksčiau siūlytus klasių modelius.

scholarly journals Using T3, an Improved Decision Tree Classifier, for Mining Stroke-related Medical Data

2007 ◽  
Vol 46 (05) ◽  
pp. 523-529 ◽  
Author(s):  
M. Saraee ◽  
B. Theodoulidis ◽  
J. A. Keane ◽  
C. Tjortjis
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Models ◽  
Medical Data ◽  
Classification Algorithm ◽  
Medical Decision ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Tree Classifier

Summary Objectives: Medical data are a valuable resource from which novel and potentially useful knowledge can be discovered by using data mining. Data mining can assist and support medical decision making and enhance clinical managementand investigative research. The objective of this work is to propose a method for building accurate descriptive and predictive models based on classification of past medical data. We also aim to compare this method with other well established data mining methods and identify strengths and weaknesses. Method: We propose T3, a decision tree classifier which builds predictive models based on known classes, by allowing for a certain amount of misclassification error in training in order to achieve better descriptive and predictive accuracy. We then experiment with a real medical data set on stroke, and various subsets, in order to identify strengths and weaknesses. We also compare performance with a very successful and well established decision tree classifier. Results: T3 demonstrated impressive performance when predicting unseen cases of stroke resulting in as little as 0.4% classification error while the state of the art decision tree classifier resulted in 33.6% classification error respectively. Conclusions: This paper presents and evaluates T3, a classification algorithm that builds decision trees of depth at most three, and results in high accuracy whilst keeping the tree size reasonably small. T3 demonstrates strong descriptive and predictive power without compromising simplicity and clarity. We evaluate T3 based on real stroke register data and compare it with C4.5, a well-known classification algorithm, showing that T3 produces significantly more accurate and readable classifiers.

scholarly journals Using Decision Tree Classifier for Analyzing Students’ Activities

2013 ◽  
Vol 6 (2) ◽  
Author(s):  
Snježana Milinković ◽  
Mirjana Maksimović
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Decision Tree ◽  
Electrical Engineering ◽  
Decision Tree Classifier ◽  
Final Exam ◽  
Data Mining Tool ◽  
Tree Classifier ◽  
Mining Tool

In this paper students’ activities data analysis in the course Introduction to programming at Faculty of Electrical Engineering in East Sarajevo is performed. Using the data that are stored in the Moodle database combined with manually collected data, the model was developed to predict students’ performance in successfully passing the final exam. The goal was to identify variables that could help teachers in predicting students’ performance and making specific recommendations for improving individual activities that could directly influence final exam successful passing. The model was created using decision tree classifier and experiments were performed using the WEKA data mining tool. The effect of input attributes on the model performances was analyzed and applying appropriate techniques a higher accuracy of the generated model was achieved.

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