Proteomics enables understanding the composition, structure, function and interactions of the entire protein complement of a cell, a tissue, or an organism under exactly defined conditions. Some factors such as stress or drug effects will change the protein pattern and cause the present or absence of a protein or gradual variation in abundances. Changes in the proteome provide a snapshot of the...

Existing work in process mining focuses on the discovery of the underlying process model from their instances. In this paper, we do not assume the existence of a single process model to which all process instances comply, and the goal is to discover a set of frequently occurring temporal patterns. Discovery of temporal patterns can be applied to various application domains to support crucial bu...

In this paper, we consider to find a set of substrings common to given strings. We define this problem as the template discovery problem which is, given a set of strings generated by some fixed but unknown pattern, to find the constant parts of the pattern. A pattern is a string over constant and variable symbols. It generates strings by replacing variables into constant strings. We assume that...

The objectives of this research work which is intimately related to pattern discovery and management are threefold: (i) handle the problem of pattern manipulation by defining operations on patterns, (ii) study the problem of enriching and updating a pattern set (e.g., concepts, rules) when changes occur in the user’s needs and the input data (e.g., object/attribute insertion or elimination, tax...

Proteomics enables understanding the composition, structure, function and interactions of the entire protein complement of a cell, a tissue, or an organism under exactly defined conditions. Some factors such as stress or drug effects will change the protein pattern and cause the present or absence of a protein or gradual variation in abundances. Changes in the proteome provide a snapshot of the...

Practical pattern classi cation and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classi ed. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a genetic algorithm. Our experiments demonstrate the feasibility of this approach for feature subse...

Practical pattern classiication and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classiied. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a genetic algorithm. Our experiments demonstrate the feasibility of this approach for feature subse...

Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-pri...

Devising fast and scalable algorithms, able to crunch huge amount of data, was for many years one of the main goals of data mining research. But then we realized that this was not enough. It does not matter how efficient such algorithms can be, the results we obtain are often of limited use in practice. Typically, the knowledge we seek is in a small pool of local patterns hidden within an ocean...

In this thesis we have developed novel methods for analyzing biological data, the primary sequences of the DNA and proteins, the microarray based gene expression data, and other functional genomics data. The main contribution is the development of the pattern discovery algorithm SPEXS, accompanied by several practical applications for analyzing real biological problems. For performing these bio...