Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175

Adaptive Resonance Theory (ART) for social media analytics

This chapter presents the ART-based clustering algorithms for social media analytics in detail. Sections 3.1 and 3.2 introduce Fuzzy ART and its clustering mechanisms, respectively, which provides a deep understanding of the base model that is used and extended for handling the social media clustering challenges. Important concepts such as vigilance region (VR) and its properties are explained and proven. Subsequently, Sects. 3.3-3.7 illustrate five types of ART adaptive resonance theory variants, each of which addresses the challenges in one social media analytical scenario, including automated parameter adaptation, user preference incorporation, short text clustering, heterogeneous data co-clustering and online streaming data indexing. The content of this chapter is several prior studies, including Probabilistic ART [15

Enrichment Procedures for Soft Clusters: A Statistical Test and its Applications

Clusters, typically mined by modeling locality of attribute spaces, are often evaluated for their ability to demonstrate ‘enrichment’ of categorical features. A cluster enrichment procedure evaluates the membership of a cluster for significant representation in pre-defined categories of interest. While classical enrichment procedures assume a hard clustering definition, in this paper we introduce a new statistical test that computes enrichments for soft clusters. We demonstrate an application of this test in refining and evaluating soft clusters for classification of remotely sensed images

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

THE ROLE OF ARTIFICIAL NEURAL NETWORKS IN DETECTION OF PULMONARY FUNCTIONAL ABNORMALITIES

Umjetna neuronska mreža je sustav temeljen na radu biološke neuronske mreže, drugim riječima, ona predstavlja oponašanje biološke neuronske mreže. Cilj ovog rada je usporediti svojstva dviju različitih verzija neuronski mrežnih ART algoritama kao što su neizravne ART i ARTFC metode korištene za klasifikaciju plućnih funkcija, otkrivanje restriktivnih, opstruktivnih i normalinih uzoraka disajnih abnormalnosti putem svake neuronske mreže s podacima prikupljenim spirometrijom. Spirometrijski podaci su prikupljeni na 150 pacijenata standardnim postupkom prikupljanja, gdje se 100 ispitanika koristi za obuku i 50 za testiranje, respektivno. Rezultati su pokazali da standardi neizravni ART algoritam raste brže od ARTFC, koji uspješno rješava problem kategorizacije proliferacija.An artificial neural network is a system based on the operation of biological neural networks, in other words, it is an emulation of the biological neural system. The objective of this study is to compare the performance of two different versions of neural network ART algorithms such as Fuzzy ART vs. ARTFC methods used for classification of pulmonary function, detecting restrictive, obstructive and normal patterns of respiratory abnormalities by means of each of the neural networks, as well as the data gathered from spirometry. The spirometry data were obtained from 150 patients by standard acquisition protocol, 100 subjects used for training and 50 subjects for testing, respectively. The results showed that the standard Fuzzy ART grows faster than ARTFC, which successfully solves the category proliferation problem

Partitioning Clustering Based on Support Vector Ranking

Postprin

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

A CLUE for CLUster Ensembles

Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and "secondary" clusterings.