Autor/Autorin: Dalal Preeti
Preeti Dalal is Ph. D. in Computer Science. She holds M. Sc. in Mathematics and Masters in Computer Applications. Also, she is a UGC NET qualified. Preeti has more than 11 years of teaching experience.
This research work proposed a framework for building efficient portfolio management using clustering of Nifty financial data through intelligent agents. The framework uses various kinds of agents, i.e. user agent, clustering agent, ranking agent, validation agent, and portfolio manager to automate portfolio management task. The user agent interacts with users and accepts user investment. Then ranking agent assigns weights to attributes and higher weighted attributes are selected for the data mining task. Clustering agents for each clustering algorithm helped to detect clusters automatically from financial data by sending a request to data agents which collects necessary financial data required for clustering task. This research work used only centroid based clustering algorithms. The clustering agents sent cluster result to a validation agent which validates clustering results. The validity of the clustering result is based on similarity and dissimilarity measure. The performance analysis of three clustering algorithm was evaluated using intra-class inertia which finds the best clustering algorithm.
Dalal, Preeti
Preeti Dalal is Ph. D. in Computer Science. She holds M. Sc. in Mathematics and Masters in Computer Applications. Also, she is a UGC NET qualified. Preeti has more than 11 years of teaching experience.
Über den Autor
Preeti Dalal is Ph. D. in Computer Science. She holds M. Sc. in Mathematics and Masters in Computer Applications. Also, she is a UGC NET qualified. Preeti has more than 11 years of teaching experience.
Klappentext
This research work proposed a framework for building efficient portfolio management using clustering of Nifty financial data through intelligent agents. The framework uses various kinds of agents, i.e. user agent, clustering agent, ranking agent, validation agent, and portfolio manager to automate portfolio management task. The user agent interacts with users and accepts user investment. Then ranking agent assigns weights to attributes and higher weighted attributes are selected for the data mining task. Clustering agents for each clustering algorithm helped to detect clusters automatically from financial data by sending a request to data agents which collects necessary financial data required for clustering task. This research work used only centroid based clustering algorithms. The clustering agents sent cluster result to a validation agent which validates clustering results. The validity of the clustering result is based on similarity and dissimilarity measure. The performance analysis of three clustering algorithm was evaluated using intra-class inertia which finds the best clustering algorithm.
