Statement of Accomplishment: Programming for Everybody (Python) 2015Michal Mlynarcik
The Programming for Everybody (#PR4E) course from the
University of Michigan School of Information introduces students to the Python programming language and studies how Python can be used to do data analysis.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
Statement of Accomplishment: Programming for Everybody (Python) 2015Michal Mlynarcik
The Programming for Everybody (#PR4E) course from the
University of Michigan School of Information introduces students to the Python programming language and studies how Python can be used to do data analysis.
HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHMaciijournal
Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.
C LUSTERING B ASED A TTRIBUTE S UBSET S ELECTION U SING F AST A LGORITHmIJCI JOURNAL
In machine learning and data mining, attribute sel
ect is the practice of selecting a subset o
f most
consequential attributes for utilize in model const
ruction. Using an attribute select method is that t
he data
encloses many redundant or extraneous attributes. W
here redundant attributes are those which sup
ply
no supplemental information than the presently
selected attributes, and impertinent attribut
es offer
no valuable information in any context
1. coursera.org
Statement of Accomplishment
WITH DISTINCTION
MAY 07, 2015
V S SANDEEP INAKOLLU
HAS SUCCESSFULLY COMPLETED
Programming for Everybody
(Python)
The Programming for Everybody (#PR4E) course from the
University of Michigan School of Information introduces students
to the Python programming language and studies how Python can
be used to do data analysis.
CHARLES SEVERANCE
CLINICAL ASSOCIATE PROFESSOR, SCHOOL OF INFORMATION
UNIVERSITY OF MICHIGAN
PLEASE NOTE: THE ONLINE OFFERING OF THIS CLASS DOES NOT REFLECT THE ENTIRE CURRICULUM OFFERED TO STUDENTS ENROLLED AT
THE UNIVERSITY OF MICHIGAN. THIS STATEMENT DOES NOT AFFIRM THAT THIS STUDENT WAS ENROLLED AS A STUDENT AT THE UNIVERSITY
OF MICHIGAN IN ANY WAY. IT DOES NOT CONFER A UNIVERSITY OF MICHIGAN GRADE; IT DOES NOT CONFER UNIVERSITY OF MICHIGAN
CREDIT; IT DOES NOT CONFER A UNIVERSITY OF MICHIGAN DEGREE; AND IT DOES NOT VERIFY THE IDENTITY OF THE STUDENT.