Discovering Frequent Generalized Episodes When Events Persist for Different Durations

Laxman, Srivatsan; Sastry, P. S.; Unnikrishnan, K. P.

doi:10.1109/tkde.2007.1055

Cited by 59 publications

(41 citation statements)

References 10 publications

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“…The frequent episode discovery framework was proposed by Mannila and colleagues (Mannila et al, 1997) and enhanced in (Laxman et al, 2007). Patnaik et al (Patnaik et al, 2008) extended previous results to the processing of neurophysiological data.…”

Section: Frequent Episode Discoverymentioning

confidence: 74%

New Trends in Biologically-Inspired Audio Coding

Pichevar¹,

Najaf-Zadeh²,

Thibault³

et al. 2010

Signal Processing

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This book chapter deals with the generation of auditory-inspired spectro-temporal features aimed at audio coding. To do so, we first generate sparse audio representations we call spikegrams, using projections on gammatone or gammachirp kernels that generate neural spikes. Unlike Fourier-based representations, these representations are powerful at identifying auditory events, such as onsets, offsets, transients and harmonic structures. We show that the introduction of adaptiveness in the selection of gammachirp kernels enhances the compression rate compared to the case where the kernels are non-adaptive. We also integrate a masking model that helps reduce bitrate without loss of perceptible audio quality. We then quantize coding values using the genetic algorithm that is more optimal than uniform quantization for this framework. We finally propose a method to extract frequent auditory objects (patterns) in the aforementioned sparse representations. The extracted frequency-domain patterns (auditory objects) help us address spikes (auditory events) collectively rather than individually. When audio compression is needed, the different patterns are stored in a small codebook that can be used to efficiently encode audio materials in a lossless way. The approach is applied to different audio signals and results are discussed and compared. This work is a first step towards the design of a high-quality auditory-inspired "object-based" audio coder.

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Section: Frequent Episode Discoverymentioning

confidence: 74%

New Trends in Biologically-Inspired Audio Coding

Pichevar¹,

Najaf-Zadeh²,

Thibault³

et al. 2010

Signal Processing

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“…As an important area in data mining, sequential rule mining has attracted a great deal of attention and many interesting methods have been proposed. Several of the frequently used methods are as follows: association rule mining [10,15], sequential pattern discovery [20,26,27,32], inter-transactional mining [8], and periodic pattern and episode mining [3,14,33]. However, few of the existing algorithms consider mining the sequential patterns with concrete time information and they either only incorporate the event sequence one after another or mine with limited time tag information.…”

Section: Challenges and Literature Reviewmentioning

confidence: 99%

Evolving temporal association rules in recommender system

Zhou

Hirasawa²

2017

Neural Comput & Applic

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This research involves implementation of genetic network programming (GNP) and ant colony optimization (ACO) to solve the sequential rule mining problem for commercial recommendations in time-related transaction databases. Excellent recommender systems should be capable of detecting the customers' preference in a proactive and efficient manner, which requires exploring customers' potential needs with an accurate and timely approach. Due to the changing nature of customers' preferences and the differences with the traditional find-allthen-prune approach, the interesting temporal association rules are extracted by the metaheuristics, genetic algorithms-based method of GNP. Additionally, a useful model is constructed using the obtained rules to forecast future customer needs and an ACO approach to evolve the online recommender system continuously. The methodology is experimentally evaluated in a real-world application by analysing the customer database of an online supermarket.

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“…(A, 1), (A, 2), (A, 3), (B, 3), (A, 6), (A, 7), (C, 8), (B, 9), (D, 11), (C, 12), 13), (B, 14), (C, 15).…”

Section: Frequent Episode Discoveryunclassified

A unified view of the apriori-based algorithms for frequent episode discovery

2011

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Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

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Discovering Frequent Generalized Episodes When Events Persist for Different Durations

Cited by 59 publications

References 10 publications

New Trends in Biologically-Inspired Audio Coding

New Trends in Biologically-Inspired Audio Coding

Evolving temporal association rules in recommender system

A unified view of the apriori-based algorithms for frequent episode discovery

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