Open Accessoptimized time series data clustering method for NN network
Author(s) -
Goli Raja Ramesh,
D Radha,
Anantula Pranayanath Reddy,
Chanda Raj Kumar,
Prathipati Ratna Kumar
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns1.5887
Subject(s) - cluster analysis , series (stratigraphy) , data mining , computer science , dynamic time warping , sort , node (physics) , cure data clustering algorithm , similarity (geometry) , correlation clustering , data stream clustering , hierarchical clustering , pattern recognition (psychology) , range (aeronautics) , time series , algorithm , artificial intelligence , machine learning , database , structural engineering , composite material , paleontology , materials science , engineering , image (mathematics) , biology
Time series data is a common sort of data that can be found in a variety of fields. Clustering time series data has a wide range of uses and has drawn scholars from several fields. A unique approach for shape-based time series clustering is proposed in this research. Using the complex network principle, it may reduce the bulk of data, enhance efficiency, and not reduce the effects. To begin, a one-nearest neighbour network is constructed using time series items that are comparable. The similarity is measured in this step using the triangle distance. Each node in the neighbour network represents a single time series object, whereas each connection indicates a neighbour relationship between nodes. Second, high-degree nodes are selected and used to cluster. Dynamic time warping distance function and hierarchical clustering algorithm are used in the clustering process. Finally, several studies are carried out using both synthetic and actual data. The findings demonstrate that the suggested approach is both efficient and effective.