Temporal data clustering examines the time series data to determine the basic structure and other characteristics of the data. Many methodologies simply process the temporal dimension of data but it still faces the many challenges for extracting useful patterns due to complex data types. In order to analyze the complex temporal data, Hybridized Gradient Descent Spectral Graph and Local-Global Louvain Clustering (HGDSG-LGLC) technique are designed. The number of temporal data is gathered from input dataset. Then the HGDSG-LGLC technique performs graph-based clustering to partitions the vertices i.e. data into different clusters depending on similarity matrix spectrum. The distance similarity is measured between the data and cluster mean. The Gradient Descent function find minimum distance between data and cluster mean. Followed by, the Local-Global Louvain method performs the merging and filtering of temporal data to connect the local and global edges of the graph with similar data. Then for each data, the change in modularity is calculated for filtering the unwanted data from its own cluster and merging it into the neighboring cluster. As a result, optimal ‘k’ numbers of clusters are obtained with higher accuracy with minimum error rate. Experimental analysis is performed with various parameters like clustering accuracy ( ), error rate ( ), computation time ( ) and space complexity ( ) with respect to number of temporal data. The proposed HGDSG-LGLC technique achieves higher and lesser , minimum as well as than conventional methods.
Abstract and discrete modeling of spatio-temporal data types