A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering

Abstract

<jats:title>Abstract</jats:title> <jats:p>Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups called clusters, such that each cluster has similar data. It also produces clusters that could be disjoint. Accuracy and efficiency are the important measures in data clustering. Several recent studies describe bio-inspired systems as information processing systems capable of some cognitive ability. However, existing popular bio-inspired algorithms for data clustering ignored good balance between exploration and exploitation for producing better clustering results. In this article, we propose a bio-inspired algorithm, namely social spider optimization (SSO), for clustering that maintains a good balance between exploration and exploitation using female and male spiders, respectively. We compare results of the proposed algorithm SSO with K means and other nature-inspired algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO) and improved bee colony optimization (IBCO). We find it to be more robust as it produces better clustering results. Although SSO solves the problem of getting stuck in the local optimum, it needs to be modified for locating the best solution in the proximity of the generated global solution. Hence, we hybridize SSO with K means, which produces good results in local searches. We compare proposed hybrid algorithms SSO+K means (SSOKC), integrated SSOKC (ISSOKC), and interleaved SSOKC (ILSSOKC) with K means+PSO (KPSO), K means+genetic algorithm (KGA), K means+artificial bee colony (KABC) and interleaved K means+IBCO (IKIBCO) and find better clustering results. We use sum of intra-cluster distances (SICD), average cosine similarity, accuracy and inter-cluster distance to measure and validate the performance and efficiency of the proposed clustering techniques.</jats:p>

A state-of-art optimization method for analyzing the tweets of earthquake-prone region

“…Experimental results showed that the proposed attribute reduction algorithm is superior to existing swarmbased considering classification accuracy while confining the number of features. SSO-based algorithms were proposed for data clustering and comparisons have been made that would contribute to the literature [1,8]. Chandran et al [31] proposed the SSO algorithm for text clustering to reduce premature convergence and deal with local minima.…”

“…Fitness characteristics of dominant males are better than non-dominant males. The offspring is produced within a particular range through mating operation between a dominant male and one or all females [1]. Each spider is demonstrated by a position in each dimension based on weight and vibrations received from the other spiders.…”

“…Analyses of these data are significant to find out problems and developing a solution. Therefore, data clustering is one of the most utilized unsupervised classification mechanisms in data mining for summarization huge amount of data-sets [1,2]. Recently, swarm intelligence algorithms such as the Artificial Bee Colony approach [3], Particle Swarm Optimization (PSO) approach [4], Ant Colony Optimization (ACO) approach [5] and evolutionary algorithms such as the Genetic Algorithm approach [6,7] have been applied by researchers to solve high-dimensional optimization problems in text clustering area.…”

“…SSO (Social Spider Optimization) is among the swarm intelligence algorithms that demonstrate the communal behavior of organisms. SSO achieves a good balance between local and global searches [1,8]. Therefore, in the proposed study, the SSO approach is used for tweet clustering.…”

A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

“…Nearly 70% of the population is female. Each spider is characterized by its position, fitness, weight, and vibrations received from other spiders (Thalamala, Reddy & Janet, 2018). In K-centroid representation, each spider has K-centroids which are associated with a list of data instances closer to it as shown in Fig.…”