Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions. Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our goal is to find tools and applications that can be used by retailers to quickly understand their data and take better business decisions. Due to the amount and complexity of data, it is not possible to do such activities manually. We witness that trends change very quickly and retailers want to be quick in adapting the change and taking actions. This needs automation of processes and using algorithms that are efficient and fast. In our work, we mine transaction data by modeling the data as graphs. We use clustering algorithms to discover communities (clusters) in the data and then use the clusters for building a recommendation system that can recommend products to customers based on their buying behavior.

<div> <div> <div> <p>The use of graph-structured data in applications is increasing day by day. In order to infer useful information from such data, fast analytics and software tools are required. One of the graph analytics techniques used is community detection. Community detection is the technique of finding structural communities within a graph. Such communities are defined as groups which have highly connected nodes and have similarities with each other. </p> <p>This research proposes a parallel heuristic for faster community detection using the parallel version of the Louvain algorithm: Grappolo. The Louvain algorithm is a hierarchical algorithm that focuses on modularity optimization. It gained popularity because of its ability to detect high-quality communities faster than the other existing community detection algorithms. However, the Louvain algorithm is a sequential algorithm. To reduce the execution time of the Louvain algorithm, a parallel version named Grappolo exists in the literature. This algorithm proposes parallel heuristics that address the challenges that occur due to parallelizing the sequential Louvain algorithm. </p> <p>In this study, the researcher is investigating if Grappolo can be further parallelized to further reduce the execution time maintaining the quality of communities detected. To evaluate the proposed heuristic, it was tested on an OpenMP multithreaded environment. It was implemented on source codes of Android malware applications. However, as compared to Grapplolo, the proposed modified version resulted in higher execution times for the inputs tested. The modularity of the communities detected was similar to the Grappolo implementation. </p> </div> </div> </div>