Weight-Based Firefly Algorithm for Document Clustering

Abstract

Existing clustering techniques have many drawbacks and this includes being trapped in a local optima. In this paper, we introduce the utilization of a new meta-heuristics algorithm, namely the Firefly algorithm (FA) to increase solution diversity. FA is a nature-inspired algorithm that is used in many optimization problems. The FA is realized in document clustering by executing it on Reuters-21578 database. The algorithm identifies documents that has the highest light intensity in a search space and represents it as a centroid. This is followed by recognizing similar documents using the cosine similarity function. Documents that are similar to the centroid are located into one cluster and dissimilar in the other. Experiments performed on the chosen dataset produce high values of Purity and F-measure. Hence, suggesting that the proposed Firefly algorithm is a possible approach in document clustering.

Document Clustering Based on Firefly Algorithm

“…The variants include the WFA (Mohammed et al, 2014) and WFA II which enhance the exploitation of WFA that leads to better clustering. Experiments are later conducted to evaluate WFA (Mohammed et al, 2014) and WFA II and the winner will be compared against three existing clustering methods; standard Particle Swarm Optimization (PSO) (Cui et al, 2005), K-means (Jain, 2010) and integrated firefly algorithm with K-means (Rui et al, 2012).…”

“…Nevertheless, the FA has not been tested on text data. Hence, we proposed WFA (Mohammed et al, 2014) that adapts the standard FA in text clustering. The pseudo code of WFA (Mohammed et al, 2014) is presented in Fig.…”

“…In WFA (Mohammed et al, 2014), each firefly represents a single document and the initial brightness, I, of the particular firefly is represented by the total weight of a document which is calculated using Equation 7. Hence, in (Mohammed et al, 2014), we proposed Equation 10:…”

“…In WFA (Mohammed et al, 2014), in order to determine the center of a cluster, clustering is based on a single condition. Such an approach produces acceptable result but it consumes large computational effort (time and complexity).…”

“…Two contributions made include the Weight-based Firefly Algorithm (WFA) (Mohammed et al, 2014) and Weight-based Firefly Algorithm II (WFA II ). The WFA for text clustering works directly without the need to define number of clusters.…”