Date of Publication

11-10-2003

Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Rachel Edita O. Roxas

Defense Panel Chair

Jose Ronello Bartolome

Defense Panel Member

Teresita C. Limoanco
Allan B. Borra

Abstract/Summary

Ssort, an adaptive comparison-based internal sorting algorithm in the same order O(n log n) comparisons as that of Classical Quicksort, has been developed. The best case and worst case time complexities of the proposed sorting algorithm have also been analyzed. It was also empirically evaluated on randomly-generated data sets with various sortedness ratios. With the application of adaptability measurement, Adaptive Ssort has improved the performance of Ssort. Its best case time complexity is O(n) comparisons while its worst case is O(n log n) comparisons. Similarly, its performance was evaluated and compared with classical Quicksort.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

TG03606

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

x, 120 leaves ; 28 cm.

Keywords

Computer algorithms; Sorting (Electronic computers); Electronic data processing

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