Numerical Representation of Symbolic Data

• Autorzy: Kozarzewski B.

Identyfikatory

DOI

10.12921/cmst.2015.21.04.008

• Języki publikacji: EN

Abstrakty

EN

A method of direct numerical representation of symbolic data is proposed. The method starts with parsing a sequence into an ordered set (spectrum) of distinct, non-overlapping short strings of symbols (words). Next, the words spectrum is mapped onto a vector of binary components in a high dimensional, linear space. The numerical representation allows for some arithmetical operations on symbolic data. Among them is a meaningful average spectrum of two sequences. As a test, the new numerical representation is used to build centroid vectors for the k-means clustering algorithm. It significantly enhanced the clustering quality. The advantage over the conventional approach is a high score of correct clustering several real character sequences like novel, DNA and protein.

Słowa kluczowe

EN

symbolic sequence; numerical representation; distance measure; clustering

• Wydawca: Institute of Bioorganic Chemistry Scientific Publishers OWN, Polish Academy of Sciences
• Czasopismo: Computational Methods in Science and Technology
• Rocznik: 2015
• Tom: Vol. 21, No. 4
• Strony: 241--249
• Opis fizyczny: Bibliogr. 7 poz., rys.

Twórcy

autor

Kozarzewski B.

• University of Information Technology and Management ul. H. Sucharskiego 2, 35-225 Rzeszów, Poland

Bibliografia

• [1] C. Notredame, Recent progress in multiple sequence alignment: a survey, Pharmacogenomics 3(1), 131-144 (2002).
• [2] M. Randic, M. Vrako, On the similarity of DNA primary sequences, Journal of Chemical Information and Computer Sciences 40, 599-606 (2000).
• [3] S. Vinga and J. Almeida, Alignment-free sequence comparison – a review, Bioinformatics, 19(4), 513-523 (2003).
• [4] A. Kelil, S. Wang, Q. Jiang, R. Brzezinski, A general measure of similarity for categorical sequences, Knowl. Inf. Syst. 24,197-220 (2010), DOI 10.1007/s10115-009-0237-8.
• [5] B. Kozarzewski, A method for nucleotide sequences analysis, CMST 18(1), 5-10 (2012).
• [6] T. Kanungo, N.S. Netanyahu, A.Y. Wu, An Efficient k-Means Clustering Algorithm: Analysis and Implementation, IEEE Trans. Pattern Analysis and Machine Intelligence 24 (7), 881-892 (2002).
• [7] B. Kozarzewski, A New Method for Symbolic Sequences Analysis, CMST 20(3), 93-100 (2014), DOI:10.12921/cmst.2014.20.03.93-100.