Time Series Clustering: A Superior Alternative for Market Basket Analysis

Tan, Swee Chuan; Lau, Jess Pei San

doi:10.1007/978-981-4585-18-7_28

Cited by 16 publications

(7 citation statements)

References 12 publications

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“…Unsupervised methods such as clustering between time series have been studied in [15]. The authors use time series clustering as an alternative to association rule mining for market basket analysis and have found it offers a much more efficient and accurate result.…”

Section: Related Workmentioning

confidence: 99%

Time series event correlation with DTW and Hierarchical Clustering methods

Mishra¹,

Shafi²,

Pathak³

2019

Preprint

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Data driven decision making is becoming increasingly an important aspect for successful business execution. More and more organizations are moving towards taking informed decisions based on the data that they are generating. Most of this data are in temporal format - time series data. Effective analysis across time series data sets, in an efficient and quick manner is a challenge. The most interesting and valuable part of such analysis is to generate insights on correlation and causation across multiple time series data sets. This paper looks at methods that can be used to analyze such data sets and gain useful insights from it, primarily in the form of correlation and causation analysis. This paper focuses on two methods for doing so, Two Sample Test with Dynamic Time Warping and Hierarchical Clustering and looks at how the results returned from both can be used to gain a better understanding of the data. Moreover, the methods used are meant to work with any data set, regardless of the subject domain and idiosyncrasies of the data set, primarily, a data agnostic approach.

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Section: Related Workmentioning

confidence: 99%

Time series event correlation with DTW and Hierarchical Clustering methods

Mishra¹,

Shafi²,

Pathak³

2019

Preprint

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“…If we cannot perform the push-forward actions, this might be due to the unavailability of an appropriate token π(σ [k] , d [k] , ) = ∅ (9) or there is no transition that would support any of the selected state-token pairs…”

Section: B: the Push-start Actionmentioning

confidence: 99%

“…Another test we conducted was on a time-series dataset comprising sales of 811 products in one year period. Tan et al in [9] are using this dataset to find alternative products. They argue that if the favorable product gets out of the stock, this will increase sales of alternative products.…”

Section: B a Synthetic Process Discovery Examplementioning

confidence: 99%

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Latent Process Discovery Using Evolving Tokenized Transducer

2019

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Today organizations capture and store an abundant amount of data from their interaction with clients, internal information systems, technical systems and sensors. Data captured this way comprises many useful insights that can be discovered by various analytical procedures and methods. Discovering regular and irregular data sequences in the captured data can reveal processes performed by the organization, which can be then assessed, measured and optimized, to achieve overall better performance, lower costs, resolve congestions, find potentially fraudulent activities and similar. Besides process discovery, capturing data sequences can give additional behavioral and tendency insights for various observations in the organization, such as sales dynamic, customer behaviour and similar. The issue is that most of the captured data intertwine multiple processes, customers, cases, products in a single data log or data stream. In this article, we propose an evolving tokenized transducer (ETT), capable of learning data sequences from a multi-contextual data log or stream. The proposed ETT is a semi-supervised relational learning method that can be used as a classifier on an unknown data log or stream, revealing previously learned data sequences. The proposed ETT was tested on multiple synthetic and real-life cases and datasets, such as dataset of retail sales sequences, hospital process log involving septic patient treatment and BPI challenge 2019 dataset. Test results are successful, revealing ETT as a prominent process discovery method.

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“…Clustering is fundamental for performing tasks as diverse as data aggregation, similarity retrieval, anomaly detection, and monitoring of time series data. Clustering of time series is an active research topic [4,12,22,42,47,55,57,60,63,66,67,68,71,72,73] however, most solutions lack a rigorous algorithmic analysis. Therefore, in this paper we study the problem of clustering time series from a theoretical point of view.…”

Section: Introductionmentioning

confidence: 99%

Clustering time series under the Fréchet distance

Driemel¹,

Krivošija²,

Sohler³

2015

Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms

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The Fréchet distance is a popular distance measure for curves. We study the problem of clustering time series under the Fréchet distance. In particular, we give (1 + ε)-approximation algorithms for variations of the following problem with parameters k and ℓ. Given n univariate time series P , each of complexity at most m, we find k time series, not necessarily from P , which we call cluster centers and which each have complexity at most ℓ, such that (a) the maximum distance of an element of P to its nearest cluster center or (b) the sum of these distances is minimized. Our algorithms have running time near-linear in the input size for constant ε, k and ℓ. To the best of our knowledge, our algorithms are the first clustering algorithms for the Fréchet distance which achieve an approximation factor of (1 + ε) or better.

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Time Series Clustering: A Superior Alternative for Market Basket Analysis

Cited by 16 publications

References 12 publications

Time series event correlation with DTW and Hierarchical Clustering methods

Time series event correlation with DTW and Hierarchical Clustering methods

Latent Process Discovery Using Evolving Tokenized Transducer

Clustering time series under the Fréchet distance

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