Discovering Students' Academic Performance Based on GPA Using K-Means Clustering Algorithm

Jamesmanoharan, J.; Ganesh, S. Hari; Felciah, M. Lovelin Ponn; Shafreenbanu, A. K.

doi:10.1109/wccct.2014.75

Cited by 21 publications

(16 citation statements)

References 1 publication

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“…They record information used to determine and generate course content quality likestudent online usage reports [20]. This intelligence is then directed to the mentors for assessment and incentive purpose for crime enforcement [21].They projected the usage of k-means clustering and selforganizing map to cluster learning objects (learning objects are educational resources. Agglomerative clustering and Density Based Spatial Clustering with Noise algorithms are used to cluster crime activities based on some predefined cases [22], and the results of these clustering are compared to find the best suitable clustering algorithm for crime detection.…”

Section: Related Workmentioning

confidence: 99%

Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

Begam¹,

Sengottuvelan²

2018

IJET

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Day to day involvement in crime becomes higher statistics for providing information against crime occurrences. A crime committed in different locations, the point of crime occurrence, strategy be analyzed very tedious using only information records. Because information collection in the form of attribute case records with direct crime rates score, so valid factor identification of crime category is a problem. By using the crime cluster in data mining technique to analyze the criminal records to propose a sentence case relative clustering algorithm (SCRCA)with addition classification rule mining algorithm to solve the crime problems. Also to use the sentence case observer technique for knowledge discovery from the crime records for proper case identification from sentence case records and to help increase the predictive accuracy. Crime examines a developing method and identifying the field in law implementation without standard definitions for correct judgment. With the expanding utilization of the clustering automated frameworks to track crimes, information examiners helping the law implementation officers and analysts to accelerate the way toward measuring crimes. The main contribution is to analyses the attribute case with relative sentences of count word analyzes factor for improving the crime prediction for categorizing crime type.

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Section: Related Workmentioning

confidence: 99%

Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

Begam¹,

Sengottuvelan²

2018

IJET

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“…Educational data mining mainly helps in studying the performance of the students based on past records [10]. Predicting the performance of students is a challenging task [11] because it involves extraction of large amounts of information from the universities containing student records [10] and analyzes the progress of each student's academic progress. Clustering in this field can be mainly used to group all the students based on their similarity measures (marks, talents, practical knowledge in a particular field, family background).…”

Section: Cluster Analysis In Education (Student's Academic Performancmentioning

confidence: 99%

A Survey on Applications of Data Mining using Clustering Techniques

Neha¹,

Vidyavathi²

2015

IJCA

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Data mining is a process that explores and analyses large data sets in order to discover meaningful patterns. Clustering is a main task of exploratory data analysis and data mining applications. Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). Clustering has wide applications in the field of medicine, business and marketing, World Wide Web, computer science, social science, educational data mining, climatology and many more. This paper mainly presents an overview of types of clustering techniques and some of the applications of data mining where clustering techniques can be applied. The main goal of clustering is to produce a good and high quality clusters that depends mainly on the similarity measure which has the ability to discover some or all hidden patterns and also make the analysis of data easy

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“…James Manoharans [8] has Discovered Students" Academic Performance Based on GPA using K-Means Clustering Algorith m.…”

Section: International Journal Of Advance Engineer Ing and Research Dmentioning

confidence: 99%

Mining Student Data to Characterize Similar Behavior Groups Using Distributed Data Mining

2015

IJAERD

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Abstract:Educational data mining is very important for characterize similar behavior groups for the student's performance Improvement. In this paper, we have provide the proposed flow that how to characterize similar behavior groups using distributed data mining. There are preprocessing the data, data mining techniques to discover association and collection of the attributes, classification, and clustering techniques. In these tasks, we extracted knowledge that describes students' behavior.

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Discovering Students' Academic Performance Based on GPA Using K-Means Clustering Algorithm

Cited by 21 publications

References 1 publication

Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses

A Survey on Applications of Data Mining using Clustering Techniques

Mining Student Data to Characterize Similar Behavior Groups Using Distributed Data Mining

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