The Parameter-less Randomized Gravitational Clustering algorithm with online clusters’ structure characterization
نویسندگان
چکیده
منابع مشابه
The Parameter-Less SOM algorithm
One of the biggest problems facing practical applications of Self-Organising Maps (SOM) is their dependence on the learning rate, the size of the neighbourhood function and the decrease of these parameters as training progresses, all of which have to be selected without firm theoretical guidance. This paper introduces a simple modification to the SOM that completely eliminates the learning rate...
متن کاملSpherical Randomized Gravitational Clustering
Circular data, i.e., data in the form of 'natural' directions or angles are very common in a number of di erent areas such as biological, meteorological, geological, and political sciences. Clustering circular data is not an easy task due to the circular geometry of the data space. Some clustering approaches, such as the spherical k-means, use the cosine distance instead of the euclidean distan...
متن کاملA parameter-less genetic algorithm
From the user’s point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press...
متن کاملThe parameter-less genetic algorithm in practice
The parameter-less genetic algorithm was introduced a couple of years ago as a way to simplify genetic algorithm operation by incorporating knowledge of parameter selection and population sizing theory in the genetic algorithm itself. This paper shows how that technique can be used in practice by applying it to a network expansion problem. The existence of the parameter-less genetic algorithm s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Progress in Artificial Intelligence
سال: 2014
ISSN: 2192-6352,2192-6360
DOI: 10.1007/s13748-014-0054-5