Abstract
In this chapter, we introduce three nature inspired swarm intelligence clustering approaches for document clustering analysis. The major challenge of today’s information society is being overwhelmed with information on any topic they are searching for. Fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the overwhelmed information. The swarm intelligence clustering algorithms use stochastic and heuristic principles discovered from observing bird flocks, fish schools, and ant food forage. Compared to the traditional clustering algorithms, the swarm algorithms are usually flexible, robust, decentralized, and self-organized. These characters make the swarm algorithms suitable for solving complex problems, such as document clustering.
TopPreliminaries
Document Representation
In most clustering algorithms, the dataset to be clustered is represented as a set of vectors X={x1, x2,….,xn}, where the vector xi corresponds to a single object and is called the feature vector. The feature vector includes proper features to represent the object. The text document objects can be represented with the Vector Space Model (VSM) (Salton, Wong, & Yang, 1975). In this model, the content of a document is formalized as a dot in the multi-dimensional space and represented by a vector d, such as d={w1, w2,….,wn}, where wi(i = 1,2,…,n) is the term weight of the term ti in one document. The term weight value represents the significance of this term in a document. To calculate the term weight, the occurrence frequency of the term within a document and in the entire set of documents must be considered. The most widely used weighting scheme combines the Term Frequency with Inverse Document Frequency (TF-IDF) (Salton & Buckley, 1988). The weight of term i in document j is given in Equation (1):
where
tfji is the number of occurrences of term
i in the document
j;
dfji indicates the term frequency in the collections of documents; and
n is the total number of documents in the collection. This weighting scheme discounts the frequent words with little discriminating power.
Key Terms in this Chapter
Vector Space Model: The vector space model (VSM) is an algebraic model used for information filtering and information retrieval. It represents natural language documents in a formal manner by the use of vectors in a multi-dimensional space. It was used for the first time by the SMART Information Retrieval System. (http://en.wikipedia.org)
Particle Swarm Optimization: The Particle Swarm Optimization (PSO) is a population based stochastic optimization technique that can be used to find an optimal, or near optimal, solution to a numerical and qualitative problem. PSO was originally developed by Eberhart and Kennedy in 1995, inspired by the social behavior of flocking birds or a school of fish.
Partitioning Clustering: The partitioning clustering method seeks to partition a collection of documents into a set of non-overlapping groups, so as to maximize the evaluation value of clustering.
Flocking Model: The Flocking Model is a bio-inspired computational model for simulating the animation of a flock of entities. The Flocking model was first proposed by Craig Reynolds in 1987.
Ant Colony Optimization: The Ant Colony Optimization (ACO) is a heuristic algorithm that is inspired from the food foraging behavior of ants. Dorigo introduced the first ACO system in his PhD thesis in 1992.
Hierarchical Clustering: The hierarchical clustering techniques produce a nested sequence of partition, with a single, all-inclusive cluster at the top and single clusters of individual points at the bottom.
Document Clustering: Document Clustering is the process dividing a set of document collections into different number of groups based on Document contents-similarity.
Swarm Intelligence: Swarm intelligence is an emerging field of biologically-inspired artificial intelligence based on the collective behavior model of social insect colonies and other animal societies.