Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval

Abstract

Abstract. In large content-based image database applications, e cient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques for text retrieval are well understood, e cient and robust indexing methodology for image retrieval is still in its infancy. In this paper, we present a non-hierarchical clustering scheme for index generation using the Rival Penalized C o m p etitive Learning (RPCL) algorithm. RPCL is a stochastic heuristic clustering method which p r o vides good cluster center approximation and is computationally e cient. Using synthetic data as well as real data, we demonstrate the recall and precision performance measurement o f nearest-neighbor feature retrieval based on the indexing structure generated by R P C L .

Introductory Chapter: Development of Data Clustering

“…There are a lot of measures of similarity or dissimilarity for data clustering. Generally, assessing the similarity of individuals in terms of the number of characteristics, which can be regarded as the points in space (e.g., a plane, the surface of a sphere, three-dimensional space, or higher-dimensional space) directly relates to the concept of distance from a geometrical viewpoint [1]. The widely used measures include Euclidean distance, Manhattan distance (also called city-block distance), and Mahalanobis distance for measuring the similarity of two data points.…”

“…Data clustering is a popular method in statistics and machine learning and is widely used to make decisions and predictions in various fields such as life science (e.g., biology, botany, zoology), medical sciences (e.g., psychiatry, pathology), behavioral and social sciences (e.g., psychology, sociology, education), earth sciences (e.g., geology, geography), engineering sciences (e.g, pattern recognition, artificial intelligence, cybernetics, electrical engineering), and information and decision sciences (e.g., information retrieval, political science, economics, marketing research, operational research) [1]. Clustering analysis aims to group individuals into a number of classes or clusters using some measure such that the individuals within classes or clusters are similar in some characteristics, and the individuals in different classes or clusters are quite distinct in some features.…”

Evolutionary Computation in Practice

“…For the task two common clustering techniques, K Means (Moore, 2004) and Rival-Penalized Clustering Learning (RPCL) (King and Lau, 1999), were considered and evaluated for their effectiveness. For both techniques a cluster centroid approach was adopted.…”

“…However the learning and de-learning rates require careful tuning to the size and density of the required clusters (King and Lau, 1999). However the learning and de-learning rates require careful tuning to the size and density of the required clusters (King and Lau, 1999).…”