Pre-processed Hierarchical Clustering for Time Series Data Streams
V. Kavitha1, A. V. Senthil Kumar2, N. Revathy3, C. Daniel Nesa Kumar4 P. Hemashree5
1Dr.V.Kavitha, PG & Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore, India.
2Dr.A.V.Senthil Kumar, PG & Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore, India.
3Dr.N.Revathy, PG & Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore, India.
4Mr.C.Daniel Nesa Kumar, PG & Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore, India.
5Mrs.P.Hemashree, PG & Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 251-254 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3961098319/19©BEIESP | DOI: 10.35940/ijrte.C3961.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The behaviour of the human body is based on the signals of chemical, electrical origin. These signals afford information that may not be directly perceptible but some information is hidden in the structure of the signal. These hidden signal information has to be translated in some way before the signals can be given useful analysis. The transformation of human body signals has been discovered useful in explaining and identifying various pathological conditions. The process of transformation is comfortable to perform since involves a limited manual effort like visual investigation of the signal generated as a result. In spite of these signals with their complexity is often considered and consequently biomedical signal processing has become an essential task for extracting significant clinical information hidden from the original signal[1]. Time series data streams constitute numerous dimensions and noisy features. Therefore, detecting the original clusters in high dimensional noisy features time series data stream is a dispute task. The challenging task involved in time series data stream are noisy and high dimensional. The existing technique is incapable of handling noisy high dimensional data stream. The most important key objective of this research part is to develop a novel pre-processing feature selection technique for discarding the noisy data is a vital successful process. Therefore this technique achieves minimum time complexity. Pre-processing feature selection is an established technique to deal with the time series data stream with noisy and high dimensional [3]. Furthermore, this innovative feature selection approach is boost up the cluster process without noisy and also it accomplishes the quality clusters with minimal time interval.
Keywords: Feature Selection, High Dimensionality, Time Series Data Stream, Preprocessing, Fuzzy Logic
Scope of the Article: Fuzzy Logic