A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means

Abstract

Abstract. This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) followed by the Ant K-means (AK) algorithm. SOM is an Artificial Neural Network (ANN), which has one of its characteristics, the nonlinear projection from a high dimensionality of the sensorial space. AK is based in the Ant Colony Optimization (ACO), which is a recently proposed meta-heuristic approach for solving hard combinatorial optimization problems. The AK algorithm modifies the K-means on locating the objects and these are then clustered according to the probabilities which in turn are updated by the pheromone. The SOMAK has a good performance when compared with some clustering techniques and reduces the computational time.

Electricity Consumption Patterns Using Som-Based Twolevel Clustering of Residential Households

“…Using K-means clustering algorithm on normalized loads of different household shown notable variance in topmost load times, which is valuable for peak consumption reduction planning [Zhang and Zimet, 2018]. [Souza et al, 2009] projected a technique of clustering which composed of two stages by combining SOM and Ant k-means to reduce computational time by the hierarchical clustering method or partitive for the large and complex sets of data.…”

Two Level Clustering for Quality Improvement using Fuzzy Subtractive Clustering and Self-Organizing Map

“…But the disadvantage of SOM is the structure of the neural network and the number of neurons in the Kohonen layer must be defined first [8]. SOM is implemented to produce protocluster in two level clustering [4,7], [10][11]. Then, the second clustering algorithms group the protocluster at the second level.…”