scholarly journals Comparative Study and Analysis of Students results using Clustering Techniques

2021 ◽  
Vol 9 (2) ◽  
pp. 835-842
Author(s):  
Mrs. Bhawna Janghel, Et. al.
Keyword(s):  
Academic Performance ◽  
Comparative Study ◽  
Data Clustering ◽  
Quality Education ◽  
Clustering Method ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
Weak Student ◽  
Using Data

In this paper using clustering method for student’s school academic performance are measured from same district. By using data clustering technique we can predict which school is best. And try to identify the weak student of particular school and will identify the result of best school. This will show which school is better for observing the techniques in disrict.The best school will be help us to making the quality education.  

Operating Point Estimation for an Absorption Process using Data Clustering Technique

2013 ◽  
Vol 13 (3) ◽  
pp. 377-384
Author(s):  
M. Amin A. Majid ◽  
Shaharin A. Sulaima ◽  
Hamdan Mokhtar ◽  
A.L. Tamiru
Keyword(s):  
Data Clustering ◽  
Point Estimation ◽  
Operating Point ◽  
Absorption Process ◽  
Clustering Technique ◽  
Using Data

Clustering Techniques and Their Applications: A Review

2020 ◽  
Vol 1 (4) ◽  
pp. 1-6
Author(s):  
Arjun Dutta
Keyword(s):  
Data Mining ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Method ◽  
Clustering Techniques ◽  
Partitional Clustering ◽  
Gene Information ◽  
Scientific Fields

This paper deals with concise study on clustering: existing methods and developments made at various times. Clustering is defined as an unsupervised learning where the targets are sorted out on the foundation of some similarity inherent among them. In the recent times, we dispense with large masses of data including images, video, social text, DNA, gene information, etc. Data clustering analysis has come out as an efficient technique to accurately achieve the task of categorizing information into sensible groups. Clustering has a deep association with researches in several scientific fields. k-means algorithm was suggested in 1957. K-mean is the most popular partitional clustering method till date. In many commercial and non-commercial fields, clustering techniques are used. The applications of clustering in some areas like image segmentation, object and role recognition and data mining are highlighted. In this paper, we have presented a brief description of the surviving types of clustering approaches followed by a survey of the areas.

scholarly journals CSFC: A New Centroid Based Clustering Method to Improve the Efficiency of Storing and Accessing Small Files in Hadoop

2020 ◽  
Vol 8 (4S5) ◽  
pp. 122-127
Keyword(s):  
Big Data ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
Memory Size

In day to day life, the computer plays a major role, due to this advancement of technology collection of data from various fields are increasing. A large amount of data is produced by various fields for every second and is not easy to process. This large amount of data is called as Big data. A large number of small files also considered as Big data. It's not easy to process and store the small files in Hadoop. In the existing methods Merging technologies and Clustering Techniques are used to combine smaller files to large files up to 128 MB before sending it to HDFS in Hadoop. In the Proposed system CSFC (Clustering Small Files based on Centroid) Clustering Technique is used without mentioning the number of Clusters previously because if the clusters are mentioned before, all the files are clubbed within the limited number of clusters. In proposing system clusters are generated by depending on the number of related files in the dataset. The relevant files are combined up to 128 MB in a cluster. If any file is not relevant to the existing cluster or if the memory size reached 128MB then-new cluster will be generated and the file will be stored. It is easy to process the related files, comparing two irrelevant files. By using this method fetching data from the data node, it produces efficient result when comparing with other clustering techniques.

An Effective Streams Clustering Method for Biomedical Signals

2010 ◽  
Vol 1 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Pimwadee Chaovalit
Keyword(s):  
Data Streams ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Healthcare Professionals ◽  
Linear Time ◽  
Monitoring Systems ◽  
Clustering Method ◽  
Biomedical Signals ◽  
Using Data ◽  
Aided Diagnosis

In the healthcare industry, the ability to monitor patients via biomedical signals assists healthcare professionals in detecting early signs of conditions such as blocked arteries and abnormal heart rhythms. Using data clustering, it is possible to interpret these signals to look for patterns that may indicate emerging or developing conditions. This can be accomplished by basing monitoring systems on a fast clustering algorithm that processes fast-paced streams of raw data effectively. This paper presents a clustering method, POD-Clus, which can be useful in computer-aided diagnosis. The proposed method clusters data streams in linear time and outperforms a competing algorithm in capturing changes of clusters in data streams.

scholarly journals Importance of an Effective Test Suite Minimization Technique in Software Testing

2019 ◽  
Vol 8 (4) ◽  
pp. 3449-3460
Keyword(s):  
Fault Detection ◽  
Software Testing ◽  
Data Clustering ◽  
Test Suite ◽  
Development Phase ◽  
Detection Capability ◽  
Clustering Technique ◽  
Minimization Technique ◽  
Using Data ◽  
Important Activity

Software testing is an important activity for developing a quality end product. But it is expensive as it requires both time and cost to perform it during development phase and after its delivery. Therefore, it is required to develop some techniques in this direction in order to make software testing a desirable as well as an affordable activity. Hence, in this study we have proposed an effective method that minimizes the size and redundancy from an automated random generated test suite using data clustering technique. The proposed technique compared to other existing techniques achieves a substantial amount of reduction in the size of the test suite without affecting the fault detection capability of the test suite.

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