У роботі розглядаються основні методи кластреної оптимізації. Наводяться приклади в якому випадку який метод краще використовувати. Також вказано, які методи для вирішення яких задач були використані при побудові електронного інтерактивного атласу містаКиєва.
Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be "the process of organizing objects into groups whose members are similar in some way".
A cluster is therefore a collection of objects which are "similar" between them and are "dissimilar" to the objects belonging to other clusters.
In this case we easily identify the 4 clusters into which the data can be divided; the similarity criterion is distance: two or more objects belong to the same cluster if they are "close" according to a given distance (in this case geometrical distance). This is called distance-based clustering.
Another kind of clustering is conceptual clustering: two or more objects belong to the same cluster if this one defines a concept common to all that objects. In other words, objects are grouped according to their fit to descriptive concepts,& not according to simple similarity measures.
So, the goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? It can be shown that there is no absolute "best" criter&ion which would be independent of the final aim of the clustering. Consequently, it is the user which must supply this criterion, in such a way that the result of the clustering will suit their needs.
For instance, we could be interested in finding &representatives for homogeneous groups (data reduction), in finding "natural clusters" and describe their unknown properties ("natural" data types), in finding useful and suitable groupings ("useful" data classes) or in finding unusual data objects (&outlier detection).
Clustering algorithms may be classified like this Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering, Probabilistic Clustering. In the first case data are grouped in an exclusive way, so that if a certain datum& belongs to a definite cluster then it could not be included in another cluster.
On the contrary the second type, the overlapping clustering, uses fuzzy sets to cluster data, so that each point may belong to two or more clusters with different degre&es of membership. In this case, data will be associated to an appropriate membership value. Instead, a hierarchical clustering algorithm is based on the union between the two nearest clusters. The beginning condition is realized by setting every datu&m as a cluster. After a few iterations it reaches the final clusters wanted.
Finally, the last kind of clustering use a completely probabilistic approach. Delevoping interactive atlas we have used K-means and Hierarchical clustering algorithms.
В р&аботе рассмотрены основные методы кластерной оптимизации. Приводятся примеры в каких случаях какой метод лучше использовать. Также указано какие методы решения задач были использованы при построении электронного интерактивного атласа города Киева.
З 31.12.2014 по 01.03.2015 Наукова бібліотека читачів не обслуговує.
