A Critically Review of Data Mining Segment: A New Perspective

Bhardwaj, Brijesh Kumar

doi:10.30534/ijatcse/2019/50862019

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…It is helpful for the students to better understand and learn how to expand educational conclusions and to gain understanding from it and clarify educational phenomena [10]. Moreover, in EDM, coming from the academic settings, data which are considered "out of sight" can be mined, analyzed and explored with the applications of newer methods or schemes in consideration of the students' learnings [11].…”

Section: Related Workmentioning

confidence: 99%

Analyzing and Predicting Career Specialization using Classification Techniques

A¹

2020

IJATCSE

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Nowadays, academic institutions conduct studies to attain quality and excellence in student academic performance through Data Mining tools. This paper explores various classification techniques in predicting graduates' career specialization. The data sets used were obtained from Bulacan State University Sarmiento Campus' Information Technology graduates from 2013 to 2016. From these data, a model was created using Naïve Bayes, J48, Random Forest, and Support Vector Machine classification algorithm, with 18 attributes. Among the models built, Naïve Bayes and Random Forest algorithm yielded better accuracy rating, and acceptable ROC and RMSE values. Performance of students per subject area was also determined, and based on this; students perform satisfactorily in both soft and technical skills manifested in the highly satisfactory performance on the Internship course. However, there were few graduates who pursue a career in Networking, and measures must be undertaken to elevate performance in early Programming and Networking courses..

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Section: Related Workmentioning

confidence: 99%

Analyzing and Predicting Career Specialization using Classification Techniques

A¹

2020

IJATCSE

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“…Early diagnosis requires concrete and robust methods for determining harmful tumors from non harmful one. The objective of these desires is to manage cases for which harm has not rehashed (controlled data) similarly as case for which malady has rehashed at a specific time [25,36]. Thusly, chest infection investigative and prognostic issues are essentially in the degree of the by and large discussed request issues.…”

Section: Prediction Process Using Classificationmentioning

confidence: 99%

A Classification Process for Detection Lung Cancer at Early Stage using Machine Learning Techniques

Srivastava¹

2020

IJETER

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Data mining can be seen as a part of data innovation. It may be applied to the random data that is of value for getting our objectives. Methods of data mining like, arrangement, grouping etc. helps in using the information for further development. For analyzing the raw dataset, few applications are employed in data mining and these methods are used in variety of fields ranging from market analysis, educational institutes and disease diagnosis like in cancer. This paper shows the use of data mining based technique for diagnosis of cancer. In this examine, machine learning is a scientific analysis method for analyzing information so as to find relation between different lung malignant features, and validate them by applying the method of analysis to other existing data sets and also analyze comparative study of data mining approaches.

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“…Data mining is identified to determine veiled patterns of a precise dataset that may be helpful for improving a number of events such as product sales, strategic planning, crime, etc. [13] [14] [15]. These methods paved the thought of the authors to come up with the factors that can affect data mining to the criminality in the Philippines [10][11].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Predictive Crime Analytics in Municipal Crime Management System in Calauan, Laguna, Philippines

Asor¹

2020

IJATCSE

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Predictive crime analytics is a process of evaluating a dataset to discover veiled patterns that can be useful in forecasting crime occurrence. The application of a machine learning algorithm is widely used in developing artificial intelligence to integrate into computer systems. This project aims to develop a management system for crime records implementing predictive crime analytics in forecasting crime occurrences. Through predictive crime analytics and software development life cycle, a management information system that is capable of forecasting crime was successfully developed. A predictive model was designed using multinomial logistic regression, which acquires a totality accuracy of 86.60% and prediction confidence. It is also shown in this paper that regression is better than other classification algorithms in terms of predicting crime occurrence. Moreover, discussed in this research, among all the barangay in Calauan, Laguna. Dayap is the most vulnerable in different index crimes like rape, murder, robbery, theft, homicide, and illegal drugs.

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A Critically Review of Data Mining Segment: A New Perspective

Cited by 5 publications

References 2 publications

Analyzing and Predicting Career Specialization using Classification Techniques

Analyzing and Predicting Career Specialization using Classification Techniques

A Classification Process for Detection Lung Cancer at Early Stage using Machine Learning Techniques

Implementation of Predictive Crime Analytics in Municipal Crime Management System in Calauan, Laguna, Philippines

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