Advances in storage technology have long been driving the need for new data mining techniques. Not only are typical data sets becoming larger, but the diversity of available attributes is increasing in many problem domains. In biological applications for example, a single protein may have associated sequence-, text-, graph-, continuous and item data. Correspondingly, there is growing need for techniques to find patterns in such complex data. Many techniques exist for mapping specific types of data to vector space representations, such as the bag-of-words model for text [58] or embedding in vector spaces of graphs [94, 91]. However, there are few techniques that recognize the resulting vector space representations as units that may be combined and further processed. This research aims to mine important vector-item patterns hidden across multiple and diverse data sources. We consider sets of related continuous attributes as vector data and search for patterns that relate a vector attribute to one or more items. The presence of an item set defines a subset of vectors that may or may not show unexpected density fluctuations. Two types of vector-item pattern mining algorithms have been developed, namely histogram-based vector-item pattern mining algorithms and point distribution vector-item pattern mining algorithms. In histogram-based vector-item pattern mining algorithms, a vector-item pattern is significant or important if its density histogram significantly differs from what is expected for a random subset of transactions, using χ² goodness-of-fit test or effect size analysis. For point distribution vector-item pattern mining algorithms, a vector-item pattern is significant if its probability density function (PDF) has a big KullbackLeibler divergence from random subsamples. We have applied the vector-item pattern mining algorithms to several application areas, and by comparing with other state-of-art algorithms we justify the effectiveness and efficiency of the algorithms.

No description available.

Note:

No description available.

No description available.

Random-access memory (RAM) testing to detect unrestricted pattern-sensitive faults (PSFs) is impractical due to the size of the memory checking sequence required. A formal model for restricted PSFs in RAMs called adjacent-pattern interference faults (APIFs) is presented. A test algorithm capable of detecting APIFs in RAMs requiring a minimum number of memory operations is then developed.

Feature selection is a critical part of any pattern classification problem. There are many methods for selecting a good set of features. However, for problems where features must be selected from a massive set, most of these methods have accuracy rates that are very low, or computational complexities that are very high. While for some pattern classification problems it might be reasonable to reduce a massive set of features by using application specific information, in problems such as dynamic signature verification this is not possible. Several existing feature selectors are evaluated including the Karhunen-Loeve, SELECT, exhaustive, accelerated, "n best features", sequential forward search, sequential backward search, and the "plus q - take away r" feature selection methods. Each of these methods has particular problems, making them poor candidates for selection of features from a massive set. A General-Purpose Reduction-Intensive (GPRI) feature selector is proposed in this thesis. The GPRI feature selector reduces a large set of features to a small final feature set. The time complexity of the GPRI method is close to the "n best features" method; however, the accuracy rates (obtained with the features selected) far exceeds the "n best features" feature selector. Thus, the GPRI feature selector is a viable candidate for selecting features in general environments where little application specific information is available. / M.S.

No description available.

The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.

Event understanding systems, responsible for automatically generating human relatable event descriptions from video sequences, is an open problem in computer vision research that has many applications in the sports domain, such as indexing and retrieval systems for sports video. Background modelling and shot classification of broadcast video are important steps in event understanding in video sequences. Shot classification seeks to identify shots, i.e. the labelling of continuous frame sequences captured by a single camera action such as long shot, close-up and audience shot, while background modelling seeks to classify pixels in an image as foreground/background. Many features used for shot classification are built upon the background model therefore background modelling is an essential part of shot classification. This dissertation reports on an investigation into techniques and procedures for background modelling and classification of shots in broadcast soccer videos. Broadcast video refers to video which would typically be viewed by a person at home on their television set and imposes constraints that are often not considered in many approaches to event detection. In this work we analyse the performances of two background modelling techniques appropriate for broadcast video, the colour distance model and Gaussian mixture model. The performance of the background models depends on correctly set parameters. Some techniques offer better updating schemes and thus adapt better to the changing conditions of a game, some are shown to be more robust to changes in broadcast technique and are therefore of greater value in shot classification. Our results show the colour distance model slightly outperformed the Gaussian mixture model with both techniques performing similar to those found in literature. Many features useful for shot classification are proposed in the literature. This dissertation identifies these features and presents a detailed analysis and comparison of various features appropriate for shot classification in broadcast soccer video. Once a feature set is established, a classifier is required to determine a shot class based on the extracted features. We establish the best use of the feature set and decision tree parameters that result in the best performance and then use a combined feature set to train a neural network to classify shots. The combined feature set in conjunction with the neural network classifier proved effective in classifying shots and in some situations outperformed those techniques found in literature. / Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.