Source :
2010 10th International Conference on Intelligent Systems Design and Applications
Descriptors :
time series, , data mining , pre-processing and reduction
Abstract :
In this paper we propose a new approach based on
Symbolic Aggregate approximation (SAX), called improved
iSAX to recognize efficient and accurate discovery of the
important patterns, essential for time series data. The original
SAX approach allows a very high-quality dimensionality
reduction and distance measures to be defined on the symbolic
approach and it is based on PAA (Piecewise Aggregate
Approximation) representation for dimensionality reduction
that minimizes dimensionality. The proposed improved SAX,
called iSAX includes the Relative Frequency and K-Nearest
Neighbor (RFknn) Algorithm. The main task of the algorithm
is to determine the sufficient number of intervals represented
as symbolic (alphabet size) that can ensure efficient mining
process and a good knowledge model is obtained without
major loss of knowledge. We show that iSAX can improve
representation preciseness without losing symbolic nature of
the original SAX representation. The iSAX is compared with
the original SAX and PAA representation, and demonstrate
its quality improvement. Ten time series rainfall data sets
were used. The experimental results showed that iSAX gives
better term of representation and minimum Euclidean
Distance.
Title of Article :
Improved SAX Time Series Data Representation based on Relative Frequency and K-Nearest Neighbor Algorithm
Author/Authors :
Almahdi Mohammed Ahmed , Azuraliza Abu Bakar , Abdul Razak Hamdan
Author/Authors - جزئيات :