A NEW INTEGRATED CLUSTERING ALGORITHM GFC AND SWITCHING REGRESSIONS
Shitong Wang, Haifeng Jiang, Hongjun Lü · International Journal of Pattern Recognition and Artificial Intelligence · 2002
The switching regression problems are attracting more and more attention in a variety of disciplines such as pattern recognition, economics and databases. To solve switching regression problems, many approaches have been investigated. In this paper, we present a new integrated clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. Our theoretic analysis shows that GFC can converge to a local minimum of the object function. Our experimental results illustrate that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed. Hence GFC is a new more efficient algorithm for switching regression problems.