The aim of this research is to proficiently investigatethe time series data, a framework configuration dependenton Sliding Window Technique Improved Association Rule Mining (SWT-IARM) with Enhanced Support Vector Machine (ESVM) has been mainlyimplemented in the recent past. Though, it does not give a high accuracy for larger size of the dataset along with enormous number of attributes. In the proposed system, the pre-processing is performed utilizing Modified K-Means Clustering (MKMC). The indexing process is finished by utilizing R-tree which is utilized to give quicker outcomes. Segmentation is performed by utilizing Sliding Window Algorithm (SWA) and it reduces the cost complexity by optimal segments. At that point IARM is applied on proficient rule discovery process by generating the most frequent rules. By utilizing Improved Support Vector Machine (ISVM) classification approach, the rules are categorizedfurtherprecisely. The experimental results reveal that the suggestedagenda accomplishesbetter accuracy, time complexity and rule discovery compared with the existing system.
Volume 11 | 06-Special Issue
Pages: 447-455