Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

Gajawada, Satish

doi:10.5121/ijsea.2012.3406

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Our previous paper [1] used cause effect graph, which place a vital role in fining the root cause for automatic correction of an error, another work [2] is for college placement process, for this Cause effect graph and decision table helps college students and placement people the procedure of placement and status. Again our work further extend to find the Root Cause [3] with the help of Cause effect Graph and resolved the fixes with the help of fix schemas.…”

Section: Related Workmentioning

confidence: 99%

Root Cause Detection and Test Case Generation for Student Academics

Mamatha¹

2020

IJATCSE

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Cause-effect graph concept is used to shows the relation between the effect (outcome) and cause (condition/input). We identified and define the effect and fill the effect box and draw the spine, than constructed binart tree based code events, iterate through the tree to get the result, if the results are on the false side it will backtrack towards to the root to find the actual cause and will print. The work scan the code line by line and will show the line number where actual the problem is.We took one sample test code(Fibonacci series) to demonstrate the concept. Concentrated on the bugs such as library function inclusion, parenthessis matching and package inclusion. Once the root cause is found, these data can be used for test case generation, student academic details are used in the easiest way. We categorized the student's strength levels on the basis of their studying capabilities, regularity in class and participation activities in the Indoor or outdoor level. Depending on this categorization we analyzed the effect, then Graph is constructed, it then converted into a decision table, and lastly we conclude with test case generation. So by considering the effects, the department heads can also take prior mitigations. This study useful for testing people who involved in application related to educational institutions can have the scope to pick up the test cases directly. This paper also helps educational institutions to reason out the actual cause for their effects.

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Section: Related Workmentioning

confidence: 99%

Root Cause Detection and Test Case Generation for Student Academics

Mamatha¹

2020

IJATCSE

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“…Bertini et al (2011) discussed about visual exploration of patterns using high-dimenison data using systematization techniques that extracts qality measures. Gajawada and Toshniwal (2012) propose vinayaka, a semi-supervised projected clustering method based on DE. In this method DE optimizes a hybrid cluster validation index.…”

Section: Literature Reviewmentioning

confidence: 99%

Combination of Difference Subspace and Oppurtunistic Clustering on High Dimensional Data

Ravichandran¹,

Shanmugam²

2014

Journal of Computer Science

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Clustering is recognized as sigificant technique for analysing data and concentric effort has been taken in different domains comprises of recognition of pattern, statistical analysis and data mining for decades. Subspace clustering is developed from the group of cluster objects from all subspaces of a dataset. During clustering of objects involing higher dimension, the accuracy and effectiveness of traditional clustering algorithms are very poor, because data objects may present in different clusters involving different subspaces of differing level of dimensions. To address the above issue, a new technique termed Difference Subspace and Opportunistic Clustering (DSOC) model is presented for high dimensional data to improve the accuracy during the search process and also considers the problem of accuracy in clustering the high dimensional data. Methods for obtaining subspace and designing clustering model for DSOC are specified and demonstrated, where the subspace identify the possibility of each cluster center with the detection of attackers based on multiple locations and estimation points with derived centroid points. Through comprehensive mathematical analysis, we show that DSOC improves the accuracy in high dimensional data with efficient cluster validation obtained using different subspace and opportunistic algorithm. As validated by extensive experiments on CORTINA and Ski Resort Data Set datasets DSOC produces high quality clusters by detecting the attackers and the efficiency of DSOC outperforms previous works.

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“…e DE algorithm has already been successfully used in the fields of aerodynamic design, mechanical design, and digital filter design [26,[30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Study on the Application Scheme of Aerodynamic Coefficient Identification Based on the Differential Evolution Algorithm

Liu

Cao

Feng

et al. 2020

Mathematical Problems in Engineering

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The present paper studies the application schemes of aerodynamic coefficient identification based on the differential evolution (DE) algorithm from free flight data. Two identification schemes utilizing the DE algorithm are proposed, including the whole discrete point (WDP) scheme and the differential interval linear (DIL) scheme. Several comparative tests are conducted to study the performances of the two schemes by using the noisy simulated flight data and the measured radar data. The results show satisfactory performance in estimating the aerodynamic coefficients for the two schemes. The WDP scheme provides more accurate and robust coefficients estimates in exchange of more than two times greater computational effort compared with the DIL scheme.

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Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

Cited by 7 publications

References 30 publications

Root Cause Detection and Test Case Generation for Student Academics

Root Cause Detection and Test Case Generation for Student Academics

Combination of Difference Subspace and Oppurtunistic Clustering on High Dimensional Data

Study on the Application Scheme of Aerodynamic Coefficient Identification Based on the Differential Evolution Algorithm

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