Implementing and compiling clustering using Mac Queens alias K-means apriori algorithm

This paper aims at implementing a Symmetric Multi-Threading. The paper provides a true concurrency, also known as Symmetric Multi-Threading (IACKMA), which occur when multiple threads execute instructions during the same clock cycle. It gives high-performance to Java developers, a tremendous opportunity for speeding up programs. The proposed algorithm divides the dataset into several identical unit blocks. Then, it calculates the centroids and related statistics of patterns in each unit block to represent an approximation of the information in the unit blocks. We use this reduced data to reduce the overall time for distance calculations. We find that the clustering efficiency is closely related to determining how many blocks should be partitioned. In fact, since the algorithm uses discrete assignment rather than a set of continuous parameters, the "minimum" it reaches cannot even be properly called a local minimum. Despite these limitations, the algorithm is used frequently as a result of its ease of implementation.