Corpus ID: 212471973

Mining Customer’s Data for Vehicle Insurance Prediction System using k-Means Clustering-An Application

@inproceedings{Thakur2014MiningCD,
  title={Mining Customer’s Data for Vehicle Insurance Prediction System using k-Means Clustering-An Application},
  author={Saurabh Singh Thakur and Jamuna Kanta Sing},
  year={2014}
}
Data mining or mining customer’s data helps to discover the key characteristics from the customer’s data, and possibly use those characteristics for future prediction. The problem of selecting the “best” algorithm/parameter setting is a difficult one. However kMeans Clustering is an algorithm helps to classify or to group the objects based on attributes/features into k number of groups. A good clustering algorithm ideally should produce groups with distinct non-overlapping boundaries, although… Expand

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References

SHOWING 1-10 OF 13 REFERENCES
Using rough set theory for automatic data analysis
TLDR
The main purpose of the experimentation is to investigate the efficiency of rough set theory to the real-world data analysis problem and obtain a set of decision rules describing the dependencies between some conditions to the status of the students. Expand
Principles of Data Mining
TLDR
This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions. Expand
An efficient enhanced k-means clustering algorithm
In k-means clustering, we are given a set of n data points in d-dimensional space ℝd and an integer k and the problem is to determine a set of k points in ℝd, called centers, so as to minimize theExpand
Comparisons and validation of statistical clustering techniques for microarray gene expression data
TLDR
Six clustering algorithms are considered and it is shown that the group means produced by Diana are the closest and those produced by UPGMA are the farthest from a model profile based on a set of hand-picked genes. Expand
Profile of conjugate gradient method algorithm on the performance appraisal for a fuzzy system
The formalism of Minkwosky’s inequality in Omolehin (2007a) is used in this work fundamentally to generate the fuzzy model for system performance appraisal in two public examinations in Nigeria. TheExpand
Algorithmic approaches to clustering gene expression data
TLDR
A key step in the analysis of gene expression data is the identi cation of groups of genes that manifest similar expression patterns, which translates to the algorithmic problem of clustering gene expressionData. Expand
Learning from Data: Concepts, Theory, and Methods
  • R. Lordo
  • Computer Science
  • Technometrics
  • 2001
This book consists of a collection of new wavelet techniques that can be used to explore several types of statistical problems, including but not limited to nonparametric regression, densityExpand
Improving Academic Performance of Students by Applying Data Mining Technique,‖European
  • Journal of Scientific Research,
  • 2009
Improving Academic Performance of Students by Applying Data Mining Technique
  • Journal of Scientific Research
  • 2009
Data Mining - Concepts and Techniques
  • P. Perner
  • Computer Science
  • Künstliche Intell.
  • 2002
...
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2
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