الفهرس 
REVIEW & APPLICATIONSOnly 14 pages are availabe for public view
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Abstract
Cluster analysis is an unsupervised learning technique, Clustering techniques have been applied to
a wide variety of research problems, in the field of medicine, clustering diseases, cures for
diseases, or symptoms of diseases can lead to very useful taxonomies. In the field of psychiatry,
the correct diagnosis of clusters of symptoms such as paranoia, schizophrenia, etc. is essential
for successful therapy. In archeology, researchers have attempted to establish taxonomies of stone
tools, funeral objects, etc. by applying cluster analytic techniques. In general, whenever one
needs to classify a ”mountain” of information into manageable meaningful piles, cluster analysis is
of great utility.
The main objective of this thesis is to study and evaluate some of the cluster analysis algorithms
dedicated for digital image analysis. Cluster analysis techniqu s are a diverse collection of
techniques that can be used to classify objects. The classification has the effect of reducing the
dimensionality of a data table by reducing the number of rows (cases).are studied and compared
various agglomerative and partitioning approaches. The underlying mathematics of most of these
methods is relatively simple but requires a large number of calculations which needs an extensive
computing power. Each classification technique is based upon a particular method, as it is possible
to measure similarity and dissimilarity in a number of ways. Consequently there isn’t a special
classification technique that may be considered as the sole correct technique, although there have
been attempts to define concepts such as ’optimal’ classification.
In the present work, the techniques implemented are the K-means and
!SODATA techniques that are applied to nine images to classify these images, record the standard
deviation of each class and examine results of both classifiers. The K means technique proved to
be a very simple algorithm, whereas the !SODATA technique is highly successful at finding the
spectral clusters that are inherent in the data.
The ISODATA algorithm is similar to the k-means algorithm with the distinct difference that the
ISODATA algorithm allows for different number of clusters while the k-means assumes that the number
of clusters is known a priori, ISODATA is method includes merging clusters if they are too small
and splitting clusters that are too large. So it does not need to know priori exactly number of
clusters, it is enough to give a range of detected values. So the I SODAT A is considered advanced
version of the k-means but it is time consuming.