1. A New Support Vector Finder Method Based on Triangular Calculations
9th International Conference on Information and Knowledge Technology (IKT 2017)
A New Support Vector Finder Method
Based on Triangular Calculations
and K-means Clustering
Seyed Muhammad Hossein Mousavi
S.Younes MiriNezhad
Atiye Mirmoini
Bu Ali Sina University
Department of Computer Engineering

2. ABSTRACT Classification SVM Least Squares Linear discriminant analysis
K-means clustering
Find the best Support vectors

3. INTRODUCTION Pattern Recognition K-means Clustering Support Vector
Pattern recognition methods separate intended patterns out of a bunch of data, using prior knowledge about patterns or statistical information of data.
By reducing classification samples, make classification process easier and faster.
Finding Support vector samples is a pre-processing for some classification algorithms.

4. K-means Clustering Solve clustering problem
Unsupervised learning algorithms
K-means Procedure
Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.
Assign each object to the group that has the closest centroid.
When all objects have been assigned, recalculate the positions of the K centroids.
Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated.

5. Support Vector Support Vector
An algorithm that implements classification, is known as a classifier.
Some classification algorithms like SVM, uses pre-processing to find best discriminant hyper-planes between classes and one of these pre-processing is finding Support vector samples.
Support vectors are marginal samples between classes which have the most closeness to other classes and closest to the separating hyper-plane.

6. 01 02 03 SOME OF PREVIOUS WORKS Support Vector Machine (SVM)
In 1963 was invented and uses Karush–Kuhn–Tucker conditions to finding support vectors. 02
Schölkopf, Bernhard
in 2000 they proposed a new class of support vector algorithms for regression and classification, their main ideas was to eliminate the unnecessary parameters to increase the classification process 03
Hao, Pei-Yi
In 2010, they proposed a modification of v-support vector machines (v-SVM) for regression and classification and the use of a parametric insensitive/margin model with an arbitrary shape.

7. PROBLEMS AND CHALLENGE
noisy data
outlier data

8. PROPOSED METHOD Select boundary sample by k-means
Reduce sample number
Delete outlier data
n clusters in each class
Find clusters center
Calculate distances
Eliminate unfit clusters

9. PROPOSED METHOD η ζ calculate three angles Select
three points to make triangular
calculate triangle area
𝑇𝑟𝑖𝑎𝑛𝑔𝑙𝑒 𝐴𝑟𝑒𝑎= 1 2 𝑏∗ℎ
Finds support vectors based on triangular calculations, like calculating triangle angles, area and defining threshold for them.
Angle A =cos 𝐴 = 𝑏 2 + 𝑐 2 − 𝑎 2 2(𝑏∗𝑐 η ζ

10. EXPERIMENTAL RESULTS proposed method on random data ζ=4 η=150
Experiment Databases
Fisheriris
Wine
Using K-means before main process on random data
7 cluster
ζ=4
η=150
Dataset
Fisherir is
Wine
Attribute Types
Real
Integer, Real
Number of Instances
150
178
Number of Attributes 4 13
Number of Classses 3
Year
1988
1991
Using K-means after main process on random data
7 cluster
ζ=4
η=150

11. Calculating area for every three vertexes
Start
Calculating area for every three vertexes
Selecting samples based on area threshold(ζ)
Calculating angle for every corners of triangles
Filtering samples based on, angle threshold(η)
Classification (Method one) || Classification (Method two)
End

14. CONCLUSION Proposed method can find proper support vectors
It could also be used for data reduction process
Combines with K-means clustering method

15. Thanks ؟