81,337 research outputs found
Method based on data mining techniques for breast cancer recurrence analysis
Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role according to the literature consulted, a great variety of dataset oriented to the analysis of the disease has been generated, in this research the Breast Cancer dataset was used, the purpose of the proposed research is to submit comparison of the J48 and randomforest, NaiveBayes and NaiveBayes Simple, SMO Poli-kernel and SMO RBF-Kernel classification algorithms, integrated with the Simple K-Means cluster algorithm for the generation of a model that allows the successful classification of patients who are or Non-recurring breast cancer after having previously undergone surgery for the treatment of said disease, finally the methods that obtained the best levels were SMO Poly-Kernel + Simple K-Means 98.5% of Precision, 98.5% recall, 98.5% TPRATE and 0.2% FPRATE. The results obtained suggest the possibility of using intelligent computational tools based on data mining methods for the detection of breast cancer recurrence in patients who had previously undergone surgery
A customizable multi-agent system for distributed data mining
We present a general Multi-Agent System framework for
distributed data mining based on a Peer-to-Peer model. Agent
protocols are implemented through message-based asynchronous
communication. The framework adopts a dynamic load balancing
policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances
An Investigation into the Merger of Stochastic Diffusion Search and Particle Swarm Optimisation
This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs
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