Name
Abstract | ||
|---|---|---|
Given a finite set of patterns, a clustered-clump is a maximal overlapping set of occurrences of such patterns. Several solutions have been presented for identifying clustered-clumps based on statistical, probabilistic, and most recently, formal language theory techniques. Here, motivated by applications in molecular biology and computer vision, we present efficient algorithms, using String Algorithm techniques, to identify clustered-clumps in a given text. The proposed algorithms compute in O(n + m) time the occurrences of all clusteredclumps for a given set of degenerate patterns (P) over tilde and/or degenerate text (T) over tilde of total lengths m and n, respectively; such that the total number of non-solid symbols in (P) over tilde and (T) over tilde is bounded by a fixed positive integer d. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-44944-9_45 | ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2016 |
Keywords | Field | DocType |
Conservative degenerate string, Pattern, Overlapping occurrences, Clustered-clump | Integer,Degenerate energy levels,Discrete mathematics,Finite set,Formal language,Pattern recognition,Computer science,Tilde,Artificial intelligence,Probabilistic logic,Bounded function,Computation | Conference |
Volume | ISSN | Citations |
475 | 1868-4238 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Costas S. Iliopoulos | 1 | 1534 | 167.43 |
| Ritu Kundu | 2 | 17 | 3.76 |
| Manal Mohamed | 3 | 102 | 12.62 |