THE CLUSTER ANALYSIS APPROACH FOR CLASSIFICATION OF ANDHRA PRADESH ON THE BASIS OF RAINFALL

The ave rage linkllBe method of cluster analys is was appl ied (or classifying the di stricts of AndhraPrad esh on th e b asis of monthly rainfall recorded. in diff ere nt sea so ns . The meth od of cluste ring h as the ad va nta geof least subjectivity in clu ster formati on unlike th at of the pri nci pa l components method. Th e a nalysis was ca rriedou t seasonwise on th e basis of 30 )~ ars of monthly rain fa ll data covering th e yea rs 1961-62 to 1~91. [I was foundthat the di stri cts ofAndh ra Prad esh ca n be classified iuto 5 10 7 d usters which depend on the seaso n. Ea st and WeslGod avari districts of coastal Andh ra region exh ib ited a similar pat tern in the rainfall of all the seasons: cert ain d istricts c f Telangan a region also exh ibi ted a simila r pan crn in rai nfall of all th e seasons excepting the 5Ou th~SI mensoon.In ce rtai n clu eters . there was 8 representetien u t'Ji.oJlril,,·u from all th e three ~ii ons of the Stale, Seesonwiseclust erings a re also di scu ssed .

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Classification of learning style based on Kolb's Learning Style Inventory and EEG using cluster analysis approach

2010 2nd International Congress on Engineering Education ◽

10.1109/iceed.2010.5940765 ◽

2010 ◽

Cited By ~ 7

Author(s):

Nazre Abdul Rashid ◽

Mohd. Nasir Taib ◽

Sahrim Lias ◽

Norizam Sulaiman

Keyword(s):

Cluster Analysis ◽

Learning Style ◽

Analysis Approach ◽

Learning Style Inventory

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The Cluster Analysis of Pulsar Parameters

International Astronomical Union Colloquium ◽

10.1017/s0252921100041038 ◽

1996 ◽

Vol 160 ◽

pp. 57-58

Author(s):

Igor’ F. Malov ◽

Oleg I. Malov

Keyword(s):

Cluster Analysis ◽

Rotation Axis ◽

Spectral Characteristics ◽

Position Angle ◽

Transformation Coefficient ◽

Independent Variables ◽

Previous Sample ◽

Components Method ◽

Main Components

We have used the catalogue of (Taylor et al., 1993) to investigate the problem of a classification of pulsars. Five samples have been analysed by the main components method and by methods of the cluster analysis.i)15 pulsars with 14 known parameters P, Ṗ, W50,L(Malov et al., 1994),B,z,spectral indexa, frequencies of maximumVMand minimumVcin a spectrum (Malofeev et al., 1994), an angle between magnetic moment and rotation axisβ(Malov, 1994), the transformation coefficientη=L/Ė, magnetic field near the light cylinderBlcand a maximum derivative of a position angleC= (dψ/dφ)max(Malov, 1990).ii)130 objects with 10 parameters (the spectral characteristicsα,VM,VCand the parameter C were excluded from the consideration).iii)218 pulsars with 4 known parameters P, Ṗ, L andz. These parameters can be considered as independent variables.iv)89 sources with the same 4 parameters. This sample consists of two subsamples: 65 pulsars withP> 1 s and 24 ones withP< 0.1 s.v)24 pulsars withP< 0.1 s from the previous sample.

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Relationship between school refusal behavior and social functioning: a cluster analysis approach

European Journal of Education and Psychology ◽

10.30552/ejep.v12i1.238 ◽

2019 ◽

Vol 12 (1) ◽

pp. 17 ◽

Cited By ~ 12

Author(s):

Carolina Gonzálvez ◽

Cándido J. Inglés ◽

Christopher A. Kearney ◽

Ricardo Sanmartín ◽

María Vicent ◽

...

Keyword(s):

Cluster Analysis ◽

Social Functioning ◽

Adaptive Functioning ◽

Functional Model ◽

School Refusal ◽

Assessment Scale ◽

Analysis Approach ◽

Homogeneous Groups ◽

School Refusal Behavior

On the basis of the heterogeneous casuistry that characterizes the students who refuse going to school, it is useful to have a classification of this population in homogeneous groups. For this, the aim of this study was, first, to identify by cluster analysis the profiles of school refusal behavior based on the functional model evaluated through the School Refusal Assessment Scale-Revised (SRAS-S). Secondly, it is intended to analyze if there are differences in social functioning scores according to the school refusal profiles identified. This study involved 1212 Spanish children between 8 and 11 years old (M=9.12, SD=1.05) who completed the SRAS-R to evaluate the school refusal behavior and the Child and Adolescent Social Adaptive Functioning Scale (CASAFS) to assess social functioning. Four profiles were identified: Non-school refusers, School refusers by mixed reinforcements, School refusers by tangible reinforcements and School refusers by negative reinforcements. The profile of Non-school refusers achieved the highest average scores in social functioning, while School refusers by mixed reinforcements group obtained the lowest average scores in social functioning. In general, the profiles found support the clusters identified in previous studies. The implications of social functioning on school refusal behavior are discussed.

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Neurophysiologic classification of Wilson's disease by using cluster analysis

Klinische Neurophysiologie ◽

10.1055/s-2006-939174 ◽

2006 ◽

Vol 37 (01) ◽

Author(s):

W Hermann ◽

T Villmann ◽

HJ Kühn ◽

P Baum ◽

G Reichel ◽

...

Keyword(s):

Cluster Analysis ◽

Wilson’S Disease ◽

Wilson's Disease

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Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.13 ◽

2017 ◽

Vol 7 (8) ◽

pp. 30

Author(s):

Hyeuk Kim

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Cluster Analysis ◽

Unsupervised Learning ◽

Principal Component ◽

Component Analysis ◽

Baseball Players ◽

Partitioning Around Medoids ◽

Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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A Classification of 148 U.S. Maize Inbreds: II. Validation of Cluster Analysis Based on RFLPs

Crop Science ◽

10.2135/cropsci1994.0011183x003400040006x ◽

1994 ◽

Vol 34 (4) ◽

pp. 852-865 ◽

Cited By ~ 10

Author(s):

Rita Hogan Mumm ◽

Lawrence J. Hubert ◽

J. W. Dudley

Keyword(s):

Cluster Analysis ◽

Maize Inbreds

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Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

Proceedings of the Mavlyutov Institute of Mechanics ◽

10.21662/uim2011.1.019 ◽

2011 ◽

Vol 8 (1) ◽

pp. 201-210

Author(s):

R.M. Bogdanov

Keyword(s):

Cluster Analysis ◽

Defect Density ◽

Density Variation ◽

Distance Functions ◽

Oil Pipeline ◽

Oil Pipelines ◽

Classification Of Images ◽

Main Oil Pipeline ◽

Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

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