Time series numerical association rule mining variants in smart
agriculture
- URL: http://arxiv.org/abs/2212.03669v1
- Date: Wed, 7 Dec 2022 14:35:23 GMT
- Title: Time series numerical association rule mining variants in smart
agriculture
- Abstract summary: Time series numerical association rule mining offers a new technique for extracting association rules from time series data.
This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture.
- Score: 6.313849520019119
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Numerical association rule mining offers a very efficient way of mining
association rules, where algorithms can operate directly with categorical and
numerical attributes. These methods are suitable for mining different
transaction databases, where data are entered sequentially. However, little
attention has been paid to the time series numerical association rule mining,
which offers a new technique for extracting association rules from time series
data. This paper presents a new algorithmic method for time series numerical
association rule mining and its application in smart agriculture. We offer a
concept of a hardware environment for monitoring plant parameters and a novel
data mining method with practical experiments. The practical experiments showed
the method's potential and opened the door for further extension.
Related papers
- AE SemRL: Learning Semantic Association Rules with Autoencoders [1.3886978730184498]
Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules.
We propose an Autoencoder-based approach to learn and extract association rules from time series data.
Our experiments show that semantic association rules can be extracted from a latent representation created by an Autoencoder.
arXiv Detail & Related papers (2024-03-26T22:28:43Z) - Reverse That Number! Decoding Order Matters in Arithmetic Learning [49.5504492920404]
Our work introduces a novel strategy that reevaluates the digit order by prioritizing output from the least significant digit.
Compared to the previous state-of-the-art (SOTA) method, our findings reveal an overall improvement of in accuracy while requiring only a third of the tokens typically used during training.
arXiv Detail & Related papers (2024-03-09T09:04:53Z) - Numerical Association Rule Mining: A Systematic Literature Review [2.472278204516769]
Numerical association rule mining is a widely used variant of the association rule mining technique.
We provide an in-depth study of diverse methods, algorithms, metrics, and datasets derived from 1,140 scholarly articles published from the inception of numerical association rule mining.
The paper discusses important research issues, the current status, and future possibilities of numerical association rule mining.
arXiv Detail & Related papers (2023-07-02T20:44:01Z) - An Optimal Algorithm for the Real-Valued Combinatorial Pure Exploration
of Multi-Armed Bandit [65.268245109828]
We study the real-valued pure exploration problem in the multi-armed bandit (R-CPE-MAB)
Existing methods in the R-CPE-MAB can be seen as a special case of the so-called transductive linear bandits.
We propose an algorithm named the gap-based exploration (CombGapE) algorithm, whose sample complexity matches the lower bound.
arXiv Detail & Related papers (2023-06-15T15:37:31Z) - Association Rules Mining with Auto-Encoders [5.175050215292647]
We present an auto-encoder solution to mine association rule called ARM-AE.
Our algorithm discovers high support and confidence rule set and has a better execution time than classical methods.
arXiv Detail & Related papers (2023-04-26T17:55:44Z) - Exogenous Data in Forecasting: FARM -- A New Measure for Relevance
Evaluation [62.997667081978825]
We introduce a new approach named FARM - Forward Relevance Aligned Metric.
Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series.
As a first validation step, we present the application of our FARM approach to synthetic but representative signals.
arXiv Detail & Related papers (2023-04-21T15:22:33Z) - Towards Correlated Sequential Rules [4.743965372344134]
High-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns.
The existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules.
We propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM.
arXiv Detail & Related papers (2022-10-27T17:27:23Z) - Towards Target Sequential Rules [52.4562332499155]
We propose an efficient algorithm, called targeted sequential rule mining (TaSRM)
It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the existing baseline algorithm.
arXiv Detail & Related papers (2022-06-09T18:59:54Z) - US-Rule: Discovering Utility-driven Sequential Rules [52.68017415747925]
We propose a faster algorithm, called US-Rule, to efficiently mine high-utility sequential rules.
Four tighter upper bounds (LEEU, REEU, LERSU, RERSU) and their corresponding pruning strategies are proposed.
US-Rule can achieve better performance in terms of execution time, memory consumption and scalability.
arXiv Detail & Related papers (2021-11-29T23:38:28Z) - Process Discovery for Structured Program Synthesis [70.29027202357385]
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
In this paper, we propose to use (block-) structured programs directly as target process models.
We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
arXiv Detail & Related papers (2020-08-13T10:33:10Z) - Discovering Reinforcement Learning Algorithms [53.72358280495428]
Reinforcement learning algorithms update an agent's parameters according to one of several possible rules.
This paper introduces a new meta-learning approach that discovers an entire update rule.
It includes both 'what to predict' (e.g. value functions) and 'how to learn from it' by interacting with a set of environments.
arXiv Detail & Related papers (2020-07-17T07:38:39Z) - Information cartography in association rule mining [2.535671322516818]
This study develops a method for automatic creation of metro maps of information obtained by Association Rule Mining.
Visualizing the resulted metro maps not only justifies that this is a suitable tool for presenting structured knowledge hidden in data, but also that they can tell stories to users.
arXiv Detail & Related papers (2020-02-29T21:57:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.