Методология анализа данных, основанная на многоэтапной нечеткой кластеризации

В статье предлагается методология многоэтапного применения нечетких методов автоматической классификации в задачах интеллектуального анализа и обработки многомерных данных. Приводится результат вычислительного эксперимента при анализе искусственного набора данных и сформулированы предварительные выводы.A methodology of automatic classification fuzzy methods multistage application in problems of intelligent analysis and processing of multidimensional data is proposed in the paper. The result of a numerical experiment for the analysis of the artificial data set is presented and preliminary conclusions are formulated

Charting perceptual spaces with fuzzy rules

Algorithmic music nowadays performs domain specific tasks for which classical algorithms do not offer optimal solutions or require user's expertise. Among these tasks is the extraction of models from data that offer an understanding of the underlying behavior, providing a quick and easy to use way to explore the data for first (sometimes on-the-fly) insights. Learning rules from examples is an approach often used to achieve this goal. However, together with the aforementioned requirements algorithmic composition needs to create new material so that it is perceived as consistent with the material of the data. In addition, the input data sets are usually small because the human is the bottleneck when generating them. In this contribution we present a fuzzy rule induction algorithm focused on generalizing a set of data, complying with the previous requirements, that offers good results for small data sets. For its evaluation -in a field where there are no benchmarks available - data sets obtained during user tests were used. The visual representation offered by the fuzzy chart helps to reduce the cognitive complexity of the devices used in algorithmic music. The results obtained show that this approach is promising for future developments.Peer ReviewedPostprint (author's final draft

A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds

Most of the clustering methods used in the clustering of chemical structures such as Wards, Group Average, K- means and Jarvis-Patrick, are known as hard or crisp as they partition a dataset into strictly disjoint subsets; and thus are not suitable for the clustering of chemical structures exhibiting more than one activity. Although, fuzzy clustering algorithms such as fuzzy c-means provides an inherent mechanism for the clustering of overlapping structures (objects) but this potential of the fuzzy methods which comes from its fuzzy membership functions have not been utilized effectively. In this work a fuzzy hierarchical algorithm is developed which provides a mechanism not only to benefit from the fuzzy clustering process but also to get advantage of the multiple membership function of the fuzzy clustering. The algorithm divides each and every cluster, if its size is larger than a pre-determined threshold, into two sub clusters based on the membership values of each structure. A structure is assigned to one or both the clusters if its membership value is very high or very similar respectively. The performance of the algorithm is evaluated on two bench mark datasets and a large dataset of compound structures derived from MDL MDDR database. The results of the algorithm show significant improvement in comparison to a similar implementation of the hard c-means algorithm

Outline of a new feature space deformation approach in fuzzy pattern recognition

Sposobnost prepoznavanja oblika je jedno od najznačajnijih svojstava koja karakterišu inteligentno ponašanje bioloških ili veštačkih sistema. Matematičko prepoznavanje oblika predstavlja formalnu osnovu za rešavanje ovog zadatka primenom precizno forumulisanih algoritama, koji su u najvećem delu bazirni na konvencionalnoj matematici. Kod kompleksnih sistema ovakav pristup pokazuje značajne nedostatke, prvenstveno zbog zahteva za obimnim izračunavanjima i nedovoljne robusnosti. Algoritmi koji su bazirani na 'soft computing' metodama predstavljaju dobru alternativu, otvarajući prostor za razvoj efikasnih algoritama za primenu u realnom vremenu, polazeći od činjenice da značenje sadržaja informacija nosi veću vrednost u odnosu na preciznost. U ovom radu izlaže se modifikacija i proširenje 'Subrtactive Clustering' metode, koja se pokazala efikasnom u obradi masivnih skupova oblika u realnom vremenu. Novi pristup koji je baziran prvenstveno na povezivanju parametara algoritma sa informacionim sadržajem prisutnim u skupu oblika koji se obrađuje, daje dodatne stepene slobode i omogućava da proces prepoznavanja bude vođen podacima koji se obrađuju. Predloženi algoritam je verifikovan velikim brojem simulacionih eksperimenata, od kojih su neki navedeni u ovom radu.Pattern recognition ability is one of the most important features that characterize intelligent behavior of either biological or artificial systems. Mathematical pattern recognition is the way to solve this problem using transparent algorithms that are mostly based on conventional mathematics. In complex systems it shows inadequacy, primary due to the needs for extensive computation and insufficient robustness. Algorithms based on soft computing approach offer a good alternative, giving a room to design effective tools for real-time application, having in mind that relevance (significance) prevails precision in complex systems. In this article is modified and extended subtractive clustering method, which is proven to be effective in real-time applications, when massive pattern sets is processed. The new understanding and new relations that connect parameters of the algorithm with the information underlying the pattern set are established, giving on this way the algorithm ability to be data driven to the maximum extent. Proposed algorithm is verified by a number of experiments and few of them are presented in this article

Outline of a new feature space deformation approach in fuzzy pattern recognition

Sposobnost prepoznavanja oblika je jedno od najznačajnijih svojstava koja karakterišu inteligentno ponašanje bioloških ili veštačkih sistema. Matematičko prepoznavanje oblika predstavlja formalnu osnovu za rešavanje ovog zadatka primenom precizno forumulisanih algoritama, koji su u najvećem delu bazirni na konvencionalnoj matematici. Kod kompleksnih sistema ovakav pristup pokazuje značajne nedostatke, prvenstveno zbog zahteva za obimnim izračunavanjima i nedovoljne robusnosti. Algoritmi koji su bazirani na 'soft computing' metodama predstavljaju dobru alternativu, otvarajući prostor za razvoj efikasnih algoritama za primenu u realnom vremenu, polazeći od činjenice da značenje sadržaja informacija nosi veću vrednost u odnosu na preciznost. U ovom radu izlaže se modifikacija i proširenje 'Subrtactive Clustering' metode, koja se pokazala efikasnom u obradi masivnih skupova oblika u realnom vremenu. Novi pristup koji je baziran prvenstveno na povezivanju parametara algoritma sa informacionim sadržajem prisutnim u skupu oblika koji se obrađuje, daje dodatne stepene slobode i omogućava da proces prepoznavanja bude vođen podacima koji se obrađuju. Predloženi algoritam je verifikovan velikim brojem simulacionih eksperimenata, od kojih su neki navedeni u ovom radu.Pattern recognition ability is one of the most important features that characterize intelligent behavior of either biological or artificial systems. Mathematical pattern recognition is the way to solve this problem using transparent algorithms that are mostly based on conventional mathematics. In complex systems it shows inadequacy, primary due to the needs for extensive computation and insufficient robustness. Algorithms based on soft computing approach offer a good alternative, giving a room to design effective tools for real-time application, having in mind that relevance (significance) prevails precision in complex systems. In this article is modified and extended subtractive clustering method, which is proven to be effective in real-time applications, when massive pattern sets is processed. The new understanding and new relations that connect parameters of the algorithm with the information underlying the pattern set are established, giving on this way the algorithm ability to be data driven to the maximum extent. Proposed algorithm is verified by a number of experiments and few of them are presented in this article

Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledg