Apriori algorithm is a masterstroke algorithm of association rule mining. Increased possibility of the Multicore processors is imposing us to upgrade the algorithm and applications so as to accomplishment the computational power from multiple cores finding frequent item sets is more upscale in terms of computing resources utilization and CPU power. Apriori Algorithm is used on very big data sets with high dimensionality of data. Therefore, parallel computing can be applied for mining using association rules. The process of association rule mining consists of finding frequent item sets and generating rules from the frequent item data sets. Finding frequent item sets are more expensive in terms of CPU power consumption and computing resources utilization. Thus, the majority of parallel apriori algorithm focus on parallelizing the process of discovering frequent item set. The computation of frequent item sets mainly consist of creating the candidate's generation and counting items. In parallel frequent itemsets mining algorithms addresses the issue of distributing the candidates among processors such that their creation and counting is effectively parallelized. Paper presents a comparative study of the serial and parallel mining of data sets. The Raspberry Pi is powerful, the small computer having the dimensions of credit card which is invented with the hope of an inspiring generation of learners to be creative. This computer uses ARM (Advanced RISC Machines) processor, the processor at the heart of the Raspberry Pi system is a Broadcom BCM2835 system on chip multimedia processor. Paper provides a description of the raspberry pi technology which is a very powerful computer. Also, it introduces the overall system architecture and the design of hardware components are presented in details. We also use Raspberry Pi 3 Model B 1.2 GHz 64-bit quad-core ARM Cortex-A53 and find the results using serial and parallel mining of data sets[14].