A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
This paper proposes a novel genetic clustering algorithm, called a dynamic niching quantum genetic clustering algorithm (DNQGA), which is based on the concept and principles of quantum computing, such as the qubits and superposition of states. Instead of binary representation, a boundary-coded chromosome is used. Moreover, a dynamic identification of the niches is performed at each generation to automatically evolve the optimal number of clusters as well as the cluster centers of the data set. After getting the niches of the population, a Q-gate with adaptive selection of the angle for every niches is introduced as a variation operator to drive individuals toward better solutions. Several data sets are used to demonstrate its superiority. The experimental results show that DNQGA clustering algorithm has high performance, effectiveness and flexibility.