Association rules implementation for affinity analysis between elements composing multimedia objects

The multimedia objects are a constantly growing resource in the world wide web, consequently it has generated as a necessity the design of methods and tools that allow to obtain new knowledge from the information analyzed. Association rules are a technique of Data Mining, whose purpose is to search for correlations between elements of a collection of data (data) as support for decision making from the identification and analysis of these correlations. Using algorithms such as: A priori, Frequent Parent Growth, QFP Algorithm, CBA, CMAR, CPAR, among others. On the other hand, multimedia applications today require the processing of unstructured data provided by multimedia objects, which are made up of text, images, audio and videos. For the storage, processing and management of multimedia objects, solutions have been generated that allow efficient search of data of interest to the end user, considering that the semantics of a multimedia object must be expressed by all the elements that composed of. In this article an analysis of the state of the art in relation to the implementation of the Association Rules in the processing of Multimedia objects is made, in addition the analysis of the consulted literature allows to generate questions about the possibility of generating a method of association rules for the analysis of these objects.Universidad de la Costa, Universidad Pontificia Bolivariana

Adaptive scaling of cluster boundaries for large-scale social media data clustering

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters

Método de reglas de asociación para el análisis de afinidad entre objetos de tipo texto

Maestría en IngenieríaData mining is considered a tool to extract knowledge in large volumes of information. One of the analyzes performed in data mining is the association rules, whose purpose is to look for co-occurrences among the records of a set of data. Its main application is in the analysis of market basket, where criteria for decision making are established based on the buying behavior of customers. Some of the algorithms are A priori, Frequent Parent Growth, QFP Algorithm, CBA, CMAR, CPAR. These algorithms have been designed to analyze structured databases; At present, various applications require the processing of unstructured data known as text type Objects. The purpose of this research is to generate a method to establish the relationship between the elements that make up an object of text type, for the acquisition of relevant information from the analysis of massive data sources of the same type.La minería de datos es considerada una herramienta para extraer conocimiento en grandes volúmenes de información. Uno de los análisis realizados en minería de datos son las reglas de asociación, cuyo propósito es buscar co-ocurrencias entre los registros de un conjunto de datos. Su principal aplicación se encuentra en el análisis de canasta de mercado, donde se establecen criterios para la toma de decisiones a partir del comportamiento de compra de los clientes. Algunos de los algoritmos son Apriori, Frequent Parent Growth, QFP Algorithm, CBA, CMAR, CPAR. Estos algoritmos han sido diseñados para analizar bases de datos estructuradas; en la actualidad, diversas aplicaciones requieren el procesamiento de datos no estructurados, como es el caso de los objetos de tipo texto. La investigación planteada tiene como propósito generar un método que permita establecer la relación existente entre los elementos que componen un objeto de tipo texto, para la adquisición de información relevante a partir del análisis de fuentes masivas de datos del mismo tipo

Probabilistic temporal multimedia datamining

Ph.DDOCTOR OF PHILOSOPH