This paper presents a new clustering algorithm based on similarity and dissimilarity measurements
which applied to X-Ray Images. The main phases of that paper are (pre-processing phase which
including feature extraction and find correlation among features, processing phase include applying
the new algorithm called similarity and dissimilarity clustering algorithm “SDCA” to find the best
number of clusters, and the final phase called post processing phase that including generation
ANDing Rules and verification of these rules). In this paper, we are generating knowledge base as
rules satisfy the correct rate equal 100 %. In addition, this work compares among four types of
clustering algorithm include (Fuzzy C-means clustering algorithm, Genetic algorithm, Swarm
Particle Algorithm and SDCA). Add to that, it proves by results SDCA is better from (Number of
epochs, Final number of clusters, Fitness Function and Accuracy) than other three clustering
methods used in compare