K-Means is popular and widely used clustering technique in present scenario. Many research has been done in same area for the improvement of K-Means clustering algorithm, but further investigation is always required to reveal the answers of the important questions such as ?is it possible to find optimal number of clusters dynamically while ignoring the empty clusters? or ?does the parallel execution of any clustering algorithm really improves it performance in terms of speedup?. This research presents an improved K-Means algorithm which is capable to calculate the number of clusters dynamically using Dunn?s index approach and further executes the algorithm in parallel using the capabilities of Microsoft?s Task Parallel Libraries. The original K-Means and Improved parallel modified K-Means algorithm performed for the two dimensional raw data consisting different numbers of records. From the results it is clear that the Improved K-Means is better in all the scenarios either increase the numbers of clusters or change the number of records in raw data. For the same number of input clusters and different data sets in original K-Means and Improved K-Means, the performance of Modified parallel K-Means is 20 to 50 percent better than the original K-Means in terms of Execution time and Speedup
