Discretization of Time Series Dataset Using Relative Frequency and K-Nearest Neighbor Approach

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

doi:10.1007/978-3-642-17316-5_18

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…In this section, we review the RF binning approach that is proposed in [18]. The main goal of RF is to optimize the alphabet size and minimize changes and losses in knowledge from the original information.…”

Section: Proposed Rf Methods For Alphabet Sizementioning

confidence: 99%

“…Many studies have proposed improvements to SAX. These studies [9,[14][15][16][17][18] have shown that the SAX method is still an open field of research and that new developments are required to bring new ideas to improve the performance of SAX.…”

Section: Introductionmentioning

confidence: 99%

“…Bakar et al [18] proposed an improvement to the time series data discretisation approach by using the relative frequency and K-nearest neighbor functions, which is called the RF method. The main idea of the RF method is to improve the process of determining a sufficient alphabet size while discretising the time series data.…”

Section: Introductionmentioning

confidence: 99%

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A Harmony Search Algorithm with Multi-pitch Adjustment Rate for Symbolic Time Series Data Representation

Ahmed¹,

Bakar²,

Hamdan³

2014

IJMECS

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Abstract-The representation task in time series data mining has been a critical issue because the direct manipulation of continuous, high-dimensional data is extremely difficult to complete efficiently. One time series representation approach is a symbolic representation called the Symbolic Aggregate Approximation (SAX). The main function of SAX is to find the appropriate numbers of alphabet symbols and word size that represent the time series. The aim is to achieve the largest alphabet size and maximum word length with the minimum error rate. The purpose of this study is to propose an integrated approach for a symbolic time series data representation that attempts to improve SAX by improving alphabet and word size. The Relative Frequency (RF) binning method is employed to obtain alphabet size and is integrated with the proposed Multipitch Harmony Search (HS MPAR ) algorithm to calculate the optimum alphabet and word size. RF is used because of its ability to obtain a sufficient number of intervals with a low error rate compared to other related techniques. HS MPAR algorithm is an optimization algorithm that randomly generates solutions for alphabet and word sizes and selects the best solutions. HS algorithms are compatible with multi-pitch adjustment. The integration of the RF and HS MPAR algorithms is developed to maximize information rather than to improve the error rate. The algorithms are tested on 20 standard time series datasets and are compared with the meta-heuristic algorithms GENEBLA and the original SAX algorithm. The experimental results show that the proposed method generates larger alphabet and word sizes and achieves a lower error rate than the compared methods. With larger alphabet and word sizes, the proposed method is capable of preserving important information.

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Section: Proposed Rf Methods For Alphabet Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Harmony Search Algorithm with Multi-pitch Adjustment Rate for Symbolic Time Series Data Representation

Ahmed¹,

Bakar²,

Hamdan³

2014

IJMECS

Self Cite

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“…In order to find a good algorithm for our purpose, we compared not only ChiM but also Chi2, eChi2 and mChi2 at the different levels of significance because of their availability in computing environments. In recent years, there are a few number of researches dealing with temporal data discretization for transforming the time series doi: 10.17700/jai.2017.8.1.339 16 Zeynel Cebeci, Figen Yildiz: Comparison of Chi-square based algorithms for discretization of continuous chicken egg quality traits into timely intervals (Azulay et al, 2007;Bakar et al 2010;Acosta-Mesa et al 2014; Chaudhari et al 2014). In this study, we did not consider the temporal order of variables even they were measured weekly since ANOVA analyses showed that the majority of response variables did not differ by the time points of measurement (weeks).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Chi-square based algorithms for discretization of continuous chicken egg quality traits

Cebecí¹,

Yildiz²

2017

JAI

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Discretization is a data pre-processing task for transforming continuous variables into discrete ones. In this study, four Chi-square based supervised discretization algorithms (ChiMerge, Chi2, Extended Chi2 and Modified Chi2) were compared for discretization of the fourteen continuous variables in a chicken egg quality traits dataset. We found that all of the algorithms had similar performances in term of training model accuracies obtained with C5.0 classification tree algorithm whereas ChiMerge and Chi2 were better than the remaining algorithms in term of training error rates. The numbers of intervals obtained with Chi2 tended to be large while they were very small in Extended Chi2 and Modified Chi2. The numbers of intervals from ChiMerge increased as the significance level increases whereas they were the same at all the levels of significance for the remaining algorithms. Consequently, it was revealed that ChiMerge at the significance levels of 0.05 and 0.10 was more efficient than the others and could be a better choice in discretization of the egg quality traits.

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“…This method is based on piecewise Aggregate Approximation representation PAA [5]. Many work have been proposed to improve SAX, those studies showed that SAX method still an open research area to bring and develop new ideas to improve such representation [6], [7], [8] and [9]. Daniel and Lopez [7] proposed a new algorithm for time series discretization using an approach that applied the Genetic Algorithm called GENEBLA (Genetic Entropy Based Linear Approximation).…”

Section: Introductionmentioning

confidence: 99%

Harmony Search algorithm for optimal word size in symbolic time series representation

Ahmed

Bakar

Hamdan

2011

2011 3rd Conference on Data Mining and Optimization (DMO)

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Fast and high quality time series representation is a crucial task in data mining pre-pre-processing. Recent studies have shown that most representation methods based on improving classification accuracy and compress data sets rather than maximize data information. We attempt to improve the number of SAX (time series representation method) word size and alphabet size by searching for the optimal word size. In this paper we propose a new representation algorithm (HSAX) that deals with Harmony Search algorithm (HS) to explore optimal word size (Ws) and alphabet size (a) for SAX time series. Harmony search algorithm is an optimization algorithm that generates randomly solutions (Ws, a) and select two best solutions. H SAX algorithm is developed to maximize information, rather than improve classification accuracy. We have applied HSAX algorithm on some standard time series data sets. We also compare the HSAX with other meta-heuristic GENEBLA and original SAX algorithms The experimental results showed that the HSAX Algorithm compare to SAX manage to generate more word size and achieve less error rates, whereas HSAX compared to GENEBLA the quality of error rate is comparable with the advantage that HSAX generated high number of word and alphabet size .

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Discretization of Time Series Dataset Using Relative Frequency and K-Nearest Neighbor Approach

Cited by 7 publications

References 7 publications

A Harmony Search Algorithm with Multi-pitch Adjustment Rate for Symbolic Time Series Data Representation

A Harmony Search Algorithm with Multi-pitch Adjustment Rate for Symbolic Time Series Data Representation

Comparison of Chi-square based algorithms for discretization of continuous chicken egg quality traits

Harmony Search algorithm for optimal word size in symbolic time series representation

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