3 Algorithms for Pattern Discovery 6 3.1 Exhaustive search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 Enumerating all patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.2 Exhaustive search on graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Creating long p...

Extracting useful information from structured and unstructured biological data is crucial in the health industry. Some examples include medical practitioner’s need to identify breast cancer patient in the early stage, estimate survival time of a heart disease patient, or recognize uncommon disease characteristics which suddenly appear. Currently there is an explosion in biological data availabl...

A basic task of machine learning and data mining is to automatically uncover patterns that reflect regularities in a data set. When dealing with a large database, especially when domain knowledge is not available or very weak, this can be a challenging task. The purpose of pattern discovery is to find non-random relations among events from data sets. For example, the “exclusive OR” (XOR) proble...

I this paper we describe a new method for discovering recurrent patterns in a corpus of segmented melodies. Elements of patterns in this scheme do not represent individual notes but rather represent melodic segments that are sequences of notes. A new knowledge representation for segmental patterns is designed, and a pattern discovery algorithm based on suffix trees is used to discover segmental...

We propose a framework for learning hiddenvariable models by optimizing entropies, in which entropy minimization, posterior maximization, and free energy minimization are all equivalent. Solutions for the maximum a posteriori (MAP) estimator yield powerful learning algorithms that combine all the charms of expectation-maximization and deterministic annealing. Contained as special cases are the ...

Cascading spatiotemporal pattern (CSTP) is a reflection of the underlying mechanism of natural or social processes. Identifying CSTPs provides meaningful evidence for domain experts and government agencies to forecast potential consequences of an event and plan ahead. In a natural disaster such as a hurricane, it normally results in heavy rainfall and strong winds, which will further cause floo...

A new general methodology for Musical Pattern Discovery is proposed, which tries to mimic the flow of cognitive and sub-cognitive inferences that are processed when hearing a piece of music. A brief survey shows the necessity to handle such perceptual heuristics and to specify perceptual constraints on discoverable structures. For instance, successive notes between patterns should verify a spec...

Functionally related genes often appear in each other's neighborhood on the genome; however, the order of the genes may not be the same. These groups or clusters of genes may have an ancient evolutionary origin or may signify some other critical phenomenon and may also aid in function prediction of genes. Such gene clusters also aid toward solving the problem of local alignment of genes. Simila...

III Mathematical Development 2 A Note on Information Theory . . . . . . 2 B Hidden Processes . . . . . . . . . . . . 2 C Effective States . . . . . . . . . . . . . 3 D Patterns in Ensembles . . . . . . . . . . 3 E Minimality and Prediction . . . . . . . 3 F Causal States . . . . . . . . . . . . . . 4 G Causal State-to-State Transitions . . . 4 H -Machines . . . . . . . . . . . . . . . . 4 I Opt...