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Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. Recent studies have shown that partitional clustering algorithms are more suitable for clustering large datasets. However, the K-means algorithm, the most commonly used partitional clustering algorithm, can only(More)
5 6 Abstract 7 Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, 8 we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses sto-9 chastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike(More)
In this paper, we propose a new term weighting scheme called term frequency-inverse corpus frequency (TF-ICF). It does not require term frequency information from other documents within the document collection and thus, it enables us to generate the document vectors of N streaming documents in linear time. In the context of a machine learning application,(More)
In the real world, we have to frequently deal with searching for and tracking an optimal solution in a dynamic environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the solution in a dynamic environment. Particle swarm optimization (PSO) is a population-based stochastic optimization technique, which(More)
We assess the novelty and maturity of software (SW) agent-based systems (ABS) for the Future Combat System (FCS) concept. The concept consists of troops, vehicles, communications, and weapon systems viewed as a " system of systems " [including net-centric command and control (C 2) capabilities]. In contrast to a centralized, or platform-based architecture,(More)
Analyzing and clustering large scale data set is a complex problem. One explored method of solving this problem borrows from nature, imitating the flocking behavior of birds. One limitation of this method of data clustering is its complexity O(n 2). As the number of data and feature dimensions grows, it becomes increasingly difficult to generate results in(More)
This paper reviews a method for feature analysis, segmentation, and indexing for region-based management, retrieval, and datamining of large, high-resolution geospatial libraries. Abstract We describe a method for indexing and retrieving high-resolution image regions in large geospatial data libraries. An automated feature extraction method is used that(More)