A novel Hybrid Clustering Algonthm (HCA) that incorporates the K-means into the canonical immune programming algorithm is proposed after analyzing the advantages and disadvantages of the classical k-means clustering a...A novel Hybrid Clustering Algonthm (HCA) that incorporates the K-means into the canonical immune programming algorithm is proposed after analyzing the advantages and disadvantages of the classical k-means clustering algorithm in the paper. The theory analyse and experimental results show, the algorithm not only avoids the local optima and is robust to imtialization, but also increases the convergent speed, meanwhile evidently restrains the degenerating phenomenon during the evolutionary process.展开更多
This paper presents a neural networks based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing,fuzzy computing, linguistic computing, and pattern recognition. A granul...This paper presents a neural networks based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing,fuzzy computing, linguistic computing, and pattern recognition. A granular neural network (GNN)is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. The GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relation in a database.展开更多
文摘A novel Hybrid Clustering Algonthm (HCA) that incorporates the K-means into the canonical immune programming algorithm is proposed after analyzing the advantages and disadvantages of the classical k-means clustering algorithm in the paper. The theory analyse and experimental results show, the algorithm not only avoids the local optima and is robust to imtialization, but also increases the convergent speed, meanwhile evidently restrains the degenerating phenomenon during the evolutionary process.
文摘This paper presents a neural networks based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing,fuzzy computing, linguistic computing, and pattern recognition. A granular neural network (GNN)is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. The GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relation in a database.
