Classification of data objects which is based on a
predefined knowledge of the objects is a data mining. Data
mining techniques, such as classification, are often applied on
these data to extract hidden information. Classification
algorithms have a wide range of applications like churn prediction,
fraud detection, artificial intelligence, and credit card
rating etc. Classification often faced with challenges when trying
to effectively staff and schedule works. Decision Tree
classification algorithm can be implemented in a serial or parallel
fashion based on the amount of data, memory space available on
the computer resource and scalability of the algorithm. These
decisions generate rules, which then are used to classify data.
Decision trees are the favored technique for building
understandable models. These decisions generate rules, which
then are used to classify data. Decision trees are the favored
technique for building understandable models. Here is a review
on Classification with Decision Tree Induction.
Published In : IJCAT Journal Volume 2, Issue 2
Date of Publication : 28 February 2015
Pages : 42 - 46
Figures :03
Tables : --
Publication Link :A Review on Classification Using Decision Tree
Komal Arunjeet Kaur : Department of Computer Science & Engineering, SVIET College, PTU University
Banur, Punjab, India
Dr. Lalita Bhutani : Department of Computer Science & Engineering, SVIET College, PTU University
Banur, Punjab, India
Decision Tree
Decision Tree Induction
Classification
Data Mining
Decision Tree is one of the most important tasks for
Classification. Here a review of Classification techniques
is done using various research papers in this field.
However, Review and study of research results have
shown that Decision Tree as the strong Classification
technique which consists of nodes that form a Rooted test with other classification models such as Hunt’s
Algorithm.
[1] Matthew N. Anyanwu, Sajjan G. Shiva,“Comparative
Analysis of Serial Decision Tree Classification
Algorithms”, 2009, pp 230-240.
[2] Micheline Kamber, Lara Winstone, “Generalization and
Decision Tree Induction: Efficient Classification in Data
Mining”, 1997, Pp 1-10.
[3] Raj Kumar, Dr. Anil Kumar Kapil, Anupam Bhatia,
“Modified Tree Classification in Data Mining”, 2012, pp
59-62.
[4] Lior Rokach and Oded Maimon ,”Top-Down Induction of
Decision Trees Classifiers –A Survey” IEEE
TRANSACTIONS ON SYSTEMS, MAN AND
CYBERNETICS: PART C, VOL. 1, NO. 11, November
2002, pp 1-12.
[5] Anyanwu, M., and Shiva, S. Application of Enhanced
Decision Tree Algorithm to Churn Analysis. 2009
International Conference on Artificial Intelligence and
Pattern Recognition (AIPR-09), Orlando Florida, 2009.
[7] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R.
Uthurusamy. Advances in Knowledge Discovery and Data
Mining. AAAI/MIT Press, 1996.
[8] G. Piatetsky-Shapiro andW. J. Frawley. Knowledge
Discovery in Databases. AAAI/MIT Press, 1991, pp57-70.
[9] Jaiwei Han, Micheline Kamber,”Data MiPressning
Concepts and Techniques, 2nd Edition”, Morgan
Kaufmann Publishers,2006 pp 289-301.
[10] Thair Nu Phyu,”Survey of Classification Techniques in
Data Mining”IMECS ,January2009, pp727.
