The Objective of this thesis is to talk about the usage of Fuzzy Logic in pattern recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that we choose to process the data is completely depends on the type of data. Pattern reorganization as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explaination of the process generating the data clarity seen and so on and so forth. With this basic school of thought we plunge into the world of Fuzzy Logic for the process of pattern recognition. Fuzzy Logic like any other mathematical field has its own set of principles, types, representations, usage so on and so forth. Hence our job primarily would focus to venture the ways in which Fuzzy Logic is applied to pattern recognition and knowledge of the results. That is what will be said in topics to follow. Pattern recognition is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. In the broadest sense, pattern recognition is any form of information processing for which both the input and output are different kind of data, medical records, aerial photos, market trends, library catalogs, galactic positions, fingerprints, psychological profiles, cash flows, chemical constituents, demographic features, stock options, military decisions.. Most pattern recognition techniques involve treating the data as a variable and applying standard processing techniques to it.

In this paper, we purpose a kernel-based fuzzy clustering algorithm to cluster data patterns in the feature space. Our method uses kernel functions to project data from the original space into a high dimensional feature space, and data are divided into groups though their similarities in the feature space with an incremental clustering approach. After clustering, data patterns of the same cluster in the feature space are then grouped with an arbitrarily shaped boundary in the original space. As a result, clusters with arbitrary shapes are discovered in the original space. Clustering, which can be taken as unsupervised classification, has also been utilized in resolving classification problems. So, we extend our method to process the classification problems. By working in the high dimensional feature space where the data are expected to more separable, we can discover the inner structure of the data distribution. Therefore, our method has the advantage of dealing with new incoming data pattern efficiently. The effectiveness of our method is demonstrated in the experiment.

Clustering applications dealing with perception based or biased data lead to models with non-disjunct clusters. There, objects to be clustered are allowed to belong to several clusters at the same time which results in a fuzzy clustering. It can be shown that this is equivalent to searching all maximal cliques in dynamic graphs like G_t=(V,E_t), where E_(t-1) in E_t, t=1,... ,T; E_0=(). In this article algorithms are provided to track all maximal cliques in a fully dynamic graph. It is naturally to raise the question about the maximum clique, having all maximal cliques. Therefore this article discusses potentials and drawbacks for this problem as well. (author's abstract) / Series: Working Papers on Information Systems, Information Business and Operations

Yes / Data streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift. / Ministero dell‘Istruzione, dell‘Universitá e della Ricerca.

No description available.

Building a rule-based classification system for a training data set is an important research topic in the area of data mining, knowledge discovery and expert systems. Recently, the GA-based fuzzy approach is shown to be an effective way to design an efficient evolutionary fuzzy system. In this thesis a three layers genetic algorithm with Simulated Annealing for selecting a small number of fuzzy if-then rules to building a compact fuzzy classification system will be proposed. The rule selection problem with three objectives: (1) maximize the number of correctly classified patterns, (2) minimize the number of fuzzy if-then rules, and (3) minimize the number of required features. Genetic algorithms are applied to solve this problem. A set of fuzzy if-then rules is coded into a binary string and treated as an in-dividual in genetic algorithms. The fitness of each individual is specified by three ob-jectives in the combinatorial optimization problem. Simulated annealing (SA) is op-tionally cooperated with three layers genetic algorithm to effectively select some layer control genes. The performance of the proposed method for training data and test data is ex-amined by computer simulations on the iris data set and spiral data set, and comparing the performance with the existing approaches. It is shown empirically that the pro-posed method outperforms the existing methods in the design of optimal fuzzy sys-tems.

South African metropolitan areas are experiencing rapid growth and require an increase in network infrastructure. Increased congestion negatively impacts both public and freight transport costs. The concept of City Logistics is concerned with the mobility of cities, and entails the process of optimizing urban logistics activities by concerning the social, environmental, economic, financial, and energy impacts of urban freight movement. In a costcompetitive environment, freight transporters often use sophisticated vehicle routing and scheduling applications to improve fleet utilization and reduce the cost of meeting customer demands. In this thesis, the candidate builds on the observation that vehicle routing and scheduling algorithms are inherent problem specific, with no single algorithm providing a dominant solution to all problem environments. Commercial applications mostly deploy a single algorithm in a multitude of environments which would often be better serviced by various different algorithms. This thesis algorithmically implements the ability of human decision makers to choose an appropriate solution algorithm when solving scheduling problems. The intent of the routing agent is to classify the problem as representative of a traditional problem set, based on its characteristics, and then to solve the problem with the most appropriate solution algorithm known for the traditional problem set. A not-so-artificially-intelligent-vehicle-routing-agent™ is proposed and developed in this thesis. To be considered intelligent, an agent is firstly required to be able to recognize its environment. Fuzzy c-means clustering is employed to analyze the geographic dispersion of the customers (nodes) from an unknown routing problem to determine to which traditional problem set it relates best. Cluster validation is used to classify the routing problem into a traditional problem set. Once the routing environment is classified, the agent selects an appropriate metaheuristic to solve the complex variant of the Vehicle Routing Problem. Multiple soft time windows, a heterogeneous fleet, and multiple scheduling are addressed in the presence of time-dependent travel times. A new initial solution heuristic is proposed that exploits the inherent configuration of customer service times through a concept referred to as time window compatibility. A high-quality initial solution is subsequently improved by the Tabu Search metaheuristic through both an adaptive memory, and a self-selection structure. As an alternative to Tabu Search, a Genetic Algorithm is developed in this thesis. Two new crossover mechanisms are proposed that outperform a number of existing crossover mechanisms. The first proposed mechanism successfully uses the concept of time window compatibility, while the second builds on an idea used from a different sweeping-arc heuristic. A neural network is employed to assist the intelligent routing agent to choose, based on its knowledge base, between the two metaheuristic algorithms available to solve the unknown problem at hand. The routing agent then not only solves the complex variant of the problem, but adapts to the problem environment by evaluating its decisions, and updating, or reaffirming its knowledge base to ensure improved decisions are made in future. / Thesis (PhD (Industrial Engineering))--University of Pretoria, 2007. / Industrial and Systems Engineering / PhD / unrestricted

[ES] La composición musical asistida por ordenador es un área de conocimiento que tiene sus orígenes en la segunda mitad del siglo pasado. Durante sus más de sesenta años de existencia han aparecido numerosas propuestas para abordar el problema de la creatividad artificial aplicada al ámbito de la variación musical, la emulación de estilos, la escritura automatizada de contrapunto o la composición estocástica, entre muchos otros. En la presente memoria propondremos un nuevo método para la generación computacional de variaciones y transiciones a partir de material musical proporcionado por el compositor, ya sea de carácter melódico, rítmico, armónico o tímbrico. La originalidad de nuestro método radica en la construcción de nuevos algoritmos basados en las técnicas de agrupamiento difuso, capaces incorporar el orden de los elementos de los conjuntos de datos durante el proceso de partición. Para implementar computacionalmente estas técnicas hemos diseñado el software Mercury mediante el que realizaremos distintos experimentos cuyos resultados, en forma de transiciones musicales, ilustrarán la utilidad de nuestra propuesta. Completaremos la presente investigación con la composición de la obra Transiciones difusas, para cuarteto de cuerdas, adjunta como apéndice. La metodología propuesta implica formular una nueva medida de la disimilitud musical, aplicable de forma general a la comparación de dos secuencias numéricas cualesquiera, con las que se pueda representar cualquier tupla de atributos musicales. Es posible, por tanto, aplicar esta disimilitud sobre ámbitos más teóricos como los sistemas de afinación. Finalmente propondremos diversos métodos para estimar la compatibilidad entre un conjunto de notas y un sistema de afinación generando, en última instancia, transiciones entre diferentes sistemas. / [CAT] La composició musical assistida per ordinador és una àrea de coneixement que té els seus orígens a meitat del segle passat. Durant els seus més de seixanta anys d'existència han aparegut nombroses propostes per a abordar el problema de la creativitat artificial aplicada a l'àmbit de la generació de variacions, emulació d'estils, escriptura automatitzada de contrapunt i composició de música estocàstica, entre molts altres. En aquesta memòria proposarem un nou mètode per a crear variacions i transicions entre material musical preexistent, ja siga de caràcter melòdic, rítmic, harmònic o tímbric. L'originalitat del nostre mètode radica en la construcció d'algoritmes basats en la tècnica de fuzzy clustering, capaços de realitzar agrupaments en què es té en compte l'ordre dels elements dels conjunts de dades. Per a implementar aquestes tècniques, hem dissenyat el programari Mercury mitjançant el qual es realitzaran experiments amb transicions entre melodies, ritmes i seqüències harmòniques que il·lustraran la utilitat de la nostra proposta, i que culminaran amb la composició de l'obra Transicions difuses, adjunta com a apèndix. La metodologia proposada no només té conseqüències pràctiques, sinó que implica formular una nova mesura de la dissimilitud musical, aplicable de forma general a la comparació de qualsevol parell de seqüències numèriques, que puguen representar melodies, ritmes, harmonies o timbres. Un cop establert com valorar la dissimilitud, aquesta també pot aplicar-se a àmbits molt més teòrics, com són els sistemes d'afinació. Proposarem diversos mètodes per a estimar la compatibilitat entre un conjunt de notes i un sistema d'afinació i generar, en última instància, transicions entre dos sistemes d'afinació. Aquesta tasca pot facilitar la interpretació d'obres en un sistema d'afinació diferent d'aquell per al qual van ser concebudes, sempre que s'exigisca que el nivell de compatibilitat entre els dos sistemes siga acceptable. / [EN] Computer-assisted composition is an area of knowledge that has its origins in the middle of the last century. During its more than sixty years of existence, numerous proposals have appeared to address the problem of artificial creativity applied to the field of generation of variations, emulation of styles, automated counterpoint writing, stochastic music composition, among many others. In this report we will propose a new method to create variations and transitions between pre-existing musical material, be it melodic, rhythmic, harmonic or timbre-related. The originality of our method lies in the construction of algorithms based on the technique of fuzzy clustering, capable of performing groupings in which the order of the elements of the data sets is taken into account. To implement these techniques, we designed the software Mercury through which experiments will be performed with transitions between melodies, rhythms and harmonic sequences that will illustrate the usefulness of our proposal, and that will culminate with the composition of the work Fuzzy Transitions, attached as an appendix. The proposed methodology not only has practical consequences, but also implies formulating a new measure of musical dissimilarity, applicable in a general way to the comparison of any pair of numerical sequences, which may represent melodies, rhythms, harmonies or timbres. Once established how to assess the dissimilarity, this can also be applied to much more theoretical areas, such as tuning systems. We will propose various methods to estimate the compatibility between a set of notes and an tuning system and, in the last instance, generate transitions between two tuning systems. This work can facilitate the interpretation of works in a tuning system different from that for which they were conceived, whenever it is required that the level of compatibility between both systems is acceptable. / Martínez Rodríguez, BS. (2019). El fuzzy clustering y la similitud musical: aplicación a la composición asistida por ordenador [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/134056 / TESIS

Dans cette thèse, nous proposons une méthode pour la visualisation d'ensembles chevauchant et de basé sur les diagrammes d'Euler. Les diagrammes d'Euler sont probablement les plus intuitifs pour représenter de manière schématique les ensembles qui partagent des éléments. Cette métaphore visuelle est ainsi un outil puissant en termes de visualisation d'information. Cependant, la génération automatique de ces diagrammes présente encore de nombreux problèmes difficiles. Premièrement, tous les clustering chevauchants ne peuvent pas être dessinées avec les diagrammes d'Euler classiques. Deuxièmement, la plupart des algorithmes existants permettent uniquement de représenter les diagrammes de dimensions modestes. Troisièmement, les besoins des applications réelles requièrent un processus plus fiable et plus rapide. Dans cette thèse, nous décrivons une version étendue des diagrammes d'Euler. Cette extension permet de modéliser l'ensemble des instances de la classe des clustering chevauchants. Nous proposons ensuite un algorithme automatique de génération de cette extension des diagrammes d'Euler. Enfin, nous présentons une implémentation logicielle et des expérimentations de ce nouvel algorithme.

In this paper, a similarity-driven cluster merging method is proposed for unsupervised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-driven cluster merging criterion. The similarity between clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive threshold is used for merging. In addition, a modified generalized objective function is used for prototype-based fuzzy clustering. The function includes the p-norm distance measure as well as principal components of the clusters. The number of the principal components is determined automatically from the data being clustered. The performance of this unsupervised fuzzy clustering algorithm is evaluated by several experiments of an artificial data set and a gene expression data set. / Singapore-MIT Alliance (SMA)