Evaluating Musculoskeletal Complications Among Individuals With Diabetes Mellitus

Dr. Simran Maheshwari; Dr. Subhan Quraishi; Dr. Babita Ghodke

Volume : II, Issue : VI, June - 2013

Evaluating Musculoskeletal Complications Among Individuals With Diabetes Mellitus

Dr. Simran Maheshwari, Dr. Subhan Quraishi, Dr. Babita Ghodke

Abstract :

Data mining is a widely used technique for discovering valuable information in a complex data set. Researches on Location–Based Service (LBS) have been emerging in recent years due to a wide range of potential applications. One of the active topics is the mining and prediction of mobile movements and associated transactions. Most of existing studies focus on discovering mobile patterns from the whole logs. However, this kind of patterns may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered. We propose a novel algorithm with GPS navigation method, to discover the Cluster–based Temporal Mobile Sequential Patterns (CTMSPs). Moreover, a prediction strategy is proposed to predict the subsequent mobile behaviors. In CTMSP–Mine, user clusters are constructed by a novel algorithm named Cluster–Object–based Smart Cluster Affinity Search Technique (CO–Smart–CAST) and similarities between users are evaluated by the proposed measure, Location–Based Service Alignment (LBS–Alignment). Meanwhile, a time segmentation approach is proposed to find segmenting time intervals where similar mobile characteristics exist. Here the mining and prediction of mobile behaviors with considerations of user relations and temporal property simultaneously can be predicted easily. Through experimental evaluation under various simulated conditions, the proposed methods are used to deliver excellent performance.