Finding Best Patterns Practically

Practical Algorithms for Pattern Based Linear Regression

“…The basic idea of the enumeration is similar to previous works [2][3][4][5][6]8]. The main contrivance of this paper is in the method for calculating the lower bound of the RSS score for specific subspaces of the pattern space.…”

“…Another situation is when we are given two sets of strings, where one set (positive set) consists of sequences known to possess some biological characteristic, while the other (negative set) consists of sequences known not to posses these characteristics. The problem is to find a discriminating pattern, that is, a pattern which occurs in most strings of the positive set, but does not occur in most of the strings of the negative set [2][3][4][5][6].…”

Missing pattern discovery

“…Then this research has been extended to more advanced pattern classes such as subsequence patterns [7,17], episode patterns [27,18], VLDC patterns [20], and their variations [33]. In particular, finding string patterns of some distinctive characteristic is a central task in knowledge discovery from textual data [4,32]. One extreme example of surprising patterns is missing patterns, namely, patterns that do not appear in a given text T .…”

“…Pattern discovery is a fundamental problem in Computational Biology and Bioinformatics [34,4,32,30]. A large amount of effort has been devoted to developing efficient algorithms to extract interesting, useful, and surprising substring patterns (i.e., patterns with no mismatches) from massive biological sequences [31,9].…”