Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

As location information of numerous Internet Thing (IoT) devices can be recognized through IoT sensor technology, the need for technology to efficiently analyze spatial data is increasing. One famous algorithms classifying dense into one cluster Density-Based Spatial Clustering Applications with Noise (DBSCAN). Existing DBSCAN research focuses on finding clusters in numeric or categorical data. In this paper, we propose novel problem discovering a set adjacent among results derived each keyword keyword-based algorithm. The existing algorithm has that it necessary calculate number all cases order find as result To solve problem, developed Genetic algorithm-based Keyword Matching (GKM-DBSCAN) which genetic was applied discover keyword. improve performance GKM-DBSCAN, improved general by performing operation groups. We conducted extensive experiments both real and synthetic datasets show effectiveness GKM-DBSCAN than brute-force method. experimental outperforms method up 21 times. index binarization (INB) 1.8 times faster (CNB).