Dynamic data discretization technique based on frequency and K-Nearest Neighbour algorithm

Ahmed, Almahdi Mohammed; Bakar, Azuraliza Abu; Hamdan, Abdul Razak

doi:10.1109/dmo.2009.5341919

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Ahmed et al [9] have developed the non-parametric FDIC method. In the first phase of this advanced method, initial intervals are constructed based on basic statistical frequency measures.…”

Section: Related Work 21 Discretisation Of Temporal Datamentioning

confidence: 99%

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

2021

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Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts.

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“…Ahmed et al [9] have developed the non-parametric FDIC method. In the first phase of this advanced method, initial intervals are constructed based on basic statistical frequency measures.…”

Section: Related Work 21 Discretisation Of Temporal Datamentioning

confidence: 99%

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

2021

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“…For example, Rajagopalan and Ray [9] investigated two partitioning schemes, namely, maximum entropy partitioning (MEP) (which is based on equal frequency of symbol occurrence) and uniform space partitioning (USP) (which is based on equal partitioning segment width) for one-dimensional data. Ahmed et al [17] investigated a data discretization technique called frequency dynamic interval class (FDIC), which is based on the data statistical frequency measure and k-nearest neighbor (kNN) algorithm [18]. To obtain "optimal" partitioning locations, Shannon entropy measure [2] has been used by researchers to maximize the dynamic information level in the symbol series.…”

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confidence: 99%

Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information

Ray

2017

Entropy

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This paper formulates an unsupervised algorithm for symbolization of signal time series to capture the embedded dynamic behavior. The key idea is to convert time series of the digital signal into a string of (spatially discrete) symbols from which the embedded dynamic information can be extracted in an unsupervised manner (i.e., no requirement for labeling of time series). The main challenges here are: (1) definition of the symbol assignment for the time series; (2) identification of the partitioning segment locations in the signal space of time series; and (3) construction of probabilistic finite-state automata (PFSA) from the symbol strings that contain temporal patterns. The reported work addresses these challenges by maximizing the mutual information measures between symbol strings and PFSA states. The proposed symbolization method has been validated by numerical simulation as well as by experimentation in a laboratory environment. Performance of the proposed algorithm has been compared to that of two commonly used algorithms of time series partitioning.

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Improved SAX time series data representation based on Relative Frequency and K-Nearest Neighbor Algorithm

Ahmed

Bakar

Hamdan

2010

2010 10th International Conference on Intelligent Systems Design and Applications

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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.

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Dynamic data discretization technique based on frequency and K-Nearest Neighbour algorithm

Cited by 5 publications

References 13 publications

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information

Improved SAX time series data representation based on Relative Frequency and K-Nearest Neighbor Algorithm

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