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All Journal Proceeding Information Technology JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Intelligent Decision Support System (IDSS)
Ito Wasito
Universitas Pradita, Indonesia
Computer Science & IT

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Journal : Proceeding Information Technology

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Fractal Dimension Approach for Clustering of DNA Sequences Based on Internucleotide Distance Sadikin, Mujiono; Wasito, Ito; Veritawati, Ionia
Proceeding Information Technology 2013
Publisher : Proceeding Information Technology

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Abstract – Recently, the volume of biological data increasesexponentially. Problem of utilization of this kind of data is notonly concerning to the volume but also to its various format andstorage distribution. To solve this kind of problems, someapproaches require new methods, algorithms or tools to assisthuman being in getting beneficial from the biological data. Thispaper presents the usage of fractal dimension approach based oninter nucleotide distance to cluster DNA sequences. Internucleotide distance is a numerical representation of DNAsequences which is transformed to time series signal spectrum.Higuchi Fractal Dimension (HFD) is one of methods to estimatefractal dimension which it can be utilized to reduce time seriesdimension. HFD estimation then is applied to the signal spectrumand it is treated as input to clustering method. The result of thisclustering shows that HFD approach can be considered as analternative method for dimensional reduction purposes.Compared with previous study result as ground truth, the HFDapproach clustering provides some similarities in certain degree.Tested with two kinds of data test sample, this approach results 6and 7 group similarities of 10 groups. Keywords: DNA Sequences, Fractal, Inter Nucleotide Distances
FRACTAL DIMENSION AS A DATA DIMENSIONALITY REDUCTION METHOD FOR ANOMALY DETECTION IN TIME SERIES Sadikin, Mujiono; Wasito, Ito
Proceeding Information Technology 2013
Publisher : Proceeding Information Technology

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ABSTRACT -- Many researches show that time series data is a kind of data which has biggest volume and growth compared to the others kind of data. Parallel with its huge volume and rapid growth, it is always needed new method, technique or approach to explore knowledge contained in time series data. One of many goals in data mining of time series is anomaly detection. By definition fractal is an object that has such self similarities in certain degree. This paper presents the results study of HFD (Higuchi Fractal Dimension) approach for clustering to detect the existence of an anomaly or deviation in time series data. This proposed method is applied to PT.PGN daily stock trade year 2004 to 2012 as test data. The results show that for value of discrete interval k = 5, 10, 15 their HFD tend to diverge and there are tend to converge to 2 for the greater value of k. Based on HFD time series clustering results, this approach can be used to divide the normal data and the other data that contain certain anomaly. In this study is also shown that this approach provides a better result compared to adjustment method which fill unbalance time series data with a zero value. Keywords: time series, anomaly, dimensionality reduction, fractal dimension, clustering
Co-Authors Celvyn Yulian Handri Santoso Handri Santoso Haryono . Haryono Haryono Ionia Veritawati Mikhail Aresa Latumahina Mujiono Sadikin Willy Willy