k-means classifier2

We have 4 medicines as our training data points object
and each medicine has 2 attributes. Each attribute
represents coordinate of the object. We have to
determine which medicines belong to cluster 1 and
which medicines belong to the other cluster.
Object
Attribute1 (X):
weight index
Attribute 2 (Y): pH
Medicine A
1 1
Medicine B
2 1
Medicine C
4 3
Medicine D
It is relatively efficient and fast. It computes result at
O(tkn), where n is number of objects or points, k is
number of clusters and t is number of iterations.
} k-means clustering can be applied to machine learning
or data mining
} Used on acoustic data in speech understanding to
convert waveforms into one of k categories (known as
Vector Quantization or Image Segmentation).
} Also used for choosing color palettes on old fashioned
graphical display devices and Image Quantization.

K-means algorithm is useful for undirected knowledge
discovery and is relatively simple.
} K-means has found wide spread usage in lot of fields,
ranging from unsupervised learning of neural network,
Pattern recognitions, Classification analysis, Artificial
intelligence, image processing, machine vision, and
many others.
5 4
Step 1:
} Initial value of centroids
: Suppose we use medicine
A and medicine B as the
first centroids.
}
} Let and c
1
and c
denote
the coordinate of the
centroids, then c
2
=(1,1)
and c
2
=(2,1
Objects-Centroids distance : we calculate the distance
between cluster centroid to each object. Let us use Euclidean
distance, then we have distance matrix at iteration 0 is
} Each column in the distance matrix symbolizes the object.
} The first row of the distance matrix corresponds to the distance of
each object to the first centroid and the second row is the distance
of each object to the second centroid.
} For example, distance from medicine C = (4, 3) to the first
centroid
Step 2:
is , and its distance to the second centroid
is , is et c.