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1

A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment

Del Gallo Mateo, Ciarapica Filippo Emanuele, Mazzuto Giovanni, Bevilacqua Maurizio

Management and Production Engineering Review

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2023

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Vol. 14, No. 4

56--70

EN

Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.

2

Research on transformer condition evaluation method based on association rule set pair analysis theory

Shanyuan Wang, Donglin Cai, Zhonghong Chen, Menglong Bai, Hongyu Ouyang, Xiaoyang Lai, Weichao Hu, Changke Chen, Yangchen Shi

Diagnostyka

|

2023

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Vol. 24, No. 4

art. no. 2023410

EN

Combining the advantages of set pair analysis and association rules, This paper proposes a transformer condition evaluation based on association rule with set pair analysis theory. In this paper, by analyzing the correlation between the various fault symptoms of transformer, a set of fault types is obtained. At the same time, this paper introduces variable weight formula based on the support degree and confidence degree of association rules, and finally the weight coefficients of fault types and fault symptoms are obtained. By comparing and calculating the support and confidence of association rules, while introducing variable weight formulas, the weight coefficients of fault types and fault symptoms are obtained. it effectively avoid the subjectivity of expert opinions or experiences. Based on the scalability of set pair analysis, a 5-element connection degree is adopted to improve the accuracy of handling uncertain factors in transformer fault diagnosis.

3

SDAE cleaning model of wind speed monitoring data in the Mine Monitoring System

Zhao Dan, Shen Zhiyuan, Song Zihao, Xie Lina

Archives of Mining Sciences

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2023

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Vol. 68, no. 2

251--266

EN

The effective utilisation of monitoring data of the coal mine is the core of realising intelligent mine. The complex and challenging underground environment, coupled with unstable sensors, can result in “dirty” data in monitoring information. A reliable data cleaning method is necessary to figure out how to extract high-quality information from large monitoring data sets while minimising data redundancy. Based on this, a cleaning method for sensor monitoring data based on stacked denoising autoencoders (SDAE) is proposed. The sample data of the ventilation system under normal conditions are trained by the SDAE algorithm and the upper limit of reconstruction errors is obtained by Kernel density estimation (KDE). The Apriori algorithm is used to study the correlation between monitoring data time series. By comparing reconstruction errors and error duration of test data with the upper limit of reconstruction error and tolerance time, cooperating with the correlation rule, the “dirty” data is resolved. The method is tested in the Dongshan coal mine. The experimental results show that the proposed method can not only identify the dirty data but retain the faulty information. The research provides effective basic data for fault diagnosis and disaster warning.

4

Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data

Singh Juhi, Mittal Mandeep

Management and Production Engineering Review

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2021

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Vol. 12, No. 2

57--64

EN

Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer’s next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach.

5

Checking Sets of Pure Evolving Association Rules

Combi Carlo, Rizzi Romeo, Sala Pietro

Fundamenta Informaticae

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2021

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Vol. 178, nr 4

283--313

EN

Extracting association rules from large datasets has been widely studied in many variants in the last two decades; they allow to extract relations between values that occur more “often” in a database. With temporal association rules the concept has been declined to temporal databases. In this context the “most frequent” patterns of evolution of one or more attribute values are extracted. In the temporal setting, especially where the interference betweeen temporal patterns cannot be neglected (e.g., in medical domains), there may be the case that we are looking for a set of temporal association rules for which a “significant” portion of the original database represents a consistent model for all of them. In this work, we introduce a simple and intuitive form for temporal association rules, called pure evolving association rules (PE-ARs for short), and we study the complexity of checking a set of PE-ARs over an instance of a temporal relation under approximation (i.e., a percentage of tuples that may be deleted from the original relation). As a by-product of our study we address the complexity class for a general problem on Directed Acyclic Graphs which is theoretically interesting per se.

6

Research on operation fault diagnosis algorithm of power grid equipment based on power big data

Qian Jianguo, Zhu Bingquan, Li Ying, Shi Zhengchai

Archives of Electrical Engineering

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2020

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Vol. 69, nr 4

793--800

EN

Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of supportmin. Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.

7

Comparison of Heuristics for Optimization of Association Rules

Alsolami Fawaz, Amin Talha, Moshkov Mikhail, Zielosko Beata, Żabiński Krzysztof

Fundamenta Informaticae

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2019

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Vol. 166, nr 1

1--14

EN

In this paper, seven greedy heuristics for construction of association rules are compared from the point of view of the length and coverage of constructed rules. The obtained rules are compared also with optimal ones constructed by dynamic programming algorithms. The average relative difference between length of rules constructed by the best heuristic and minimum length of rules is at most 4%. The same situation is with coverage.

8

Application of data mining techniques to find relationships between the dishes offered by a restaurant for the elaboration of combos based on the preferences of the diners

Vazquez Rosa Maria, Bonilla Edmundo, Sanchez Eduardo, Atriano Oscar, Berruecos Cinthya

Applied Computer Science

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2019

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Vol. 15, no 2

73--88

EN

Currently, blended food has been a common menu item in fast food restaurants. The sales of the fast-food industry grow thanks to several sales strategies, including the “combos”, so, specialty, regional, family and buffet restaurants are even joining combos’ promotions. This research paper presents the implementation of a system that will serve as support to elaborate combos according to the preferences of the diners using data mining techniques to find relationships between the different dishes that are offered in a restaurant. The software resulting from this research is being used by the mobile application Food Express, with which it communicates through webservices. References

9

Analiza opóźnień samolotów pasażerskich z wykorzystaniem reguł asocjacyjnych

Dramski M., Mąka M.

Autobusy : technika, eksploatacja, systemy transportowe

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2018

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R. 19, nr 12

755--757

PL

Wydajność transportu pasażerskiego w tym lotnictwa cywilnego, jest kluczowa dla światowej gospodarki. Jednym z głównych czynników oceny linii lotniczych przez pasażerów jest punktualność. Należy tu uwzględnić również fakt, że sieć połączeń między lotniskami na całym świecie jest niezwykle skomplikowana. Powyższe fakty prowadzą do wniosku, że można stworzyć narzędzie, które pomoże pasażerom planować ich podróż w sposób optymalny. W niniejszym artykule do analizy ponad 7 milionów lotów na terenie Stanów Zjednoczonych, zastosowano reguły asocjacyjne. Dane pozyskano z Departamentu Transportu USA i obejmują one loty, które odbyły się w 2008 roku.

EN

The efficiency of air passenger transport in world's economy is crucial. For this kind of flights, one of the most important features is punctuality. The network of connections between the airports, very often is significantly complicated. It leads to the conclusion that there is a need to do some research in this field which will help the passengers to plan their optimal journeys. In this paper one of the data mining techniques (association rules) was applied to the analysis of flights' delays. The data consists of over 7 millions records was taken from the US Department of Transportation (year 2008) [2]. Then the research was carried out and conclusions were described.

10

Association Rules as a Decision Making Model in the Textile Industry

Istrat V., Lalić N.

Fibres & Textiles in Eastern Europe

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2017

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Vol. 25, no. 4 (124)

8--14

EN

Sales process disfunctions in the textile industry are problems that cause loss of customers, incomplete market supply, etc. The objective of the research is to analyse transactions from the textile industry database in order to find patterns in buyers’ behavior and improve the model of decision-making. Association rules, one of the most noticeable data mining techniques, is used as methodology to learn rules and market patterns that occur in sales in the textile industry, which will enhance the decision-making process, by making it more effective and efficient. The Apriori algorithm was applied and open source software Orange was used. It has been shown using a real-life dataset containing 2000 transactions from the textile industry of the South East Europe region that the approach proposed is useful in discovering effective knowledge in data associated with sales. The study reports new interesting rules and the dependence of the following parameters: support, confidence, lift and leverage on making more customized offers in the textile industry.

PL

Nieprawidłowości w procesie sprzedaży w przemyśle tekstylnym powodują m.in. utratę klientów. Celem badania była analiza transakcji z bazy danych przemysłu tekstylnego w celu znalezienia wzorców na zachowaniach nabywców i udoskonalenia modelu decyzji. Analiza koszykowa, jako jedna z najlepiej dostrzegalnych technik kopiowania danych, jest stosowana w metodologii uczenia się reguł i wzorców rynkowych, które występują w sprzedaży w przemyśle tekstylnym zwiększając efektywność procesu podejmowania decyzji. W pracy użyto algorytmu Apriori i oprogramowania Orange. W pracy wykazano, że przy użyciu zestawu danych rzeczywistych zawierającego 2000 transakcji z przemysłu tekstylnego w regionie Europy Południowo-Wschodniej, proponowane podejście jest przydatne w odkrywaniu skutecznej wiedzy w zakresie danych związanych ze sprzedażą.

11

Wykorzystanie reguł asocjacyjnych do analizy pracy wybranego urządzenia w oddziale wydobywczym

Trzcionkowska A., Brzychczy E.

Inżynieria Mineralna

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2016

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R. 17, nr 2

113--124

PL

W artykule przedstawiono możliwości wykorzystania zaawansowanych technik eksploracji danych do analizy pracy aparatury zabezpieczeniowej stacji transformatorowej, używanej w oddziałach wydobywczych w kopalniach podziemnych. Celem analizy było znalezienie czynników, które sprzyjają występowaniu zadziałania zabezpieczeń oraz określenie, które z nich występują razem w przypadku zadziałania zabezpieczenia. W badaniach wykorzystano reguły asocjacyjne. Obliczenia zostały przeprowadzone z wykorzystaniem środowiska R oraz dodatku Rattle (Graphical User Interface for Data Mining in R). W wyniku przeprowadzonych analiz uzyskano reguły wskazujące pojawiające się zależności warunkujące pracę aparatury zabezpieczeniowej wybranej stacji transformatorowej.

EN

The article presents the application of advanced data mining techniques to analyse the operation of protection device of transformer station used in underground mines. The aim was to find the factors that favour the occurrence of protection activation. In the research association rules were used. Calculations were performed using the R environment and the addition Rattle (Graphical User Interface for Data Mining in R). As a result various rules were obtained, describing dependencies in work of protection devices for selected transformer station.

12

Logical Aspects of Dealing with Domain Knowledge in Data Mining with Association Rules

Rauch J.

Fundamenta Informaticae

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2016

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Vol. 148, nr 1/2

1--33

EN

Association rules are introduced as general relations of two general Boolean attributes derived from columns of an analysed data matrix. Expressive power of such association rules makes possible to use various items of domain knowledge in data mining. Each particular item of domain knowledge is mapped to a set of simple association rules. Simple association rules together with their logical consequences are understood as a set of consequences of a given item of domain knowledge. Such sets of consequences are used when interpreting results of a data mining procedure. Logical deduction plays a crucial role in this approach. New results on related deduction rules are presented.

13

Dynamic Programming Approach for Construction of Association Rule Systems

Alsolami F., Amin T., Chikalov I., Moshkov M., Zielosko B.

Fundamenta Informaticae

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2016

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Vol. 147, nr 2/3

159--171

EN

In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.

14

Application of Dynamic Programming Approach to Optimization of Association Rules Relative to Coverage and Length

Zielosko B.

Fundamenta Informaticae

|

2016

|

Vol. 148, nr 1/2

87--105

EN

In the paper, an application of dynamic programming approach to global optimization of approximate association rules relative to coverage and length is presented. It is an extension of the dynamic programming approach to optimization of decision rules to inconsistent tables. Experimental results with data sets from UCI Machine Learning Repository are included.

15

Comparative evaluation of the different data mining techniques used for the medical database

Kasperczuk A., Dardzińska A.

Acta Mechanica et Automatica

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2016

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Vol. 10, no. 3

233--238

EN

Data mining is the upcoming research area to solve various problems. Classification and finding association are two main steps in the field of data mining. In this paper, we use three classification algorithms: J48 (an open source Java implementation of C4.5 algorithm), Multilayer Perceptron - MLP (a modification of the standard linear perceptron) and Naïve Bayes (based on Bayes rule and a set of conditional independence assumptions) of the Weka interface. These classifiers have been used to choose the best algorithm based on the conditions of the voice disorders database. To find association rules over transactional medical database first we use apriori algorithm for frequent item set mining. These two initial steps of analysis will help to create the medical knowledgebase. The ultimate goal is to build a model, which can improve the way to read and interpret the existing data in medical database and future data as well.

16

Correlation and association analysis in wall conveyor engines diagnosis

Michalak M., Sikora B., Sobczyk J.

Studia Informatica

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2015

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Vol. 36, nr 3

43--59

EN

The paper presents the new way of machine diagnosis. The object of the research is the wall conveyor working in the coal mines. The work of the device was represented by three time series of current values of three conveyor's engines. Every startup of the conveyor work was described with almost twenty variables. The correlation analysis of over 3700 startups pointed interesting dependencies in the data. The association analysis gave sets of interpretable rules describing the proper way of conveyor work. The final prediction of the level of proper work is done on the basis of assumed number of last startups and their similarities to associations developed from the train data and represented by the association rules.

PL

Artykuł przedstawia nowy sposób analizy pracy urządzeń – w tym przypadku przenośnika ścianowego w kopalni węgla kamiennego. Praca przenośnika opisana jest za pomocą trzech przebiegów poboru prądu przez każdy z silników. Każde uruchomienie przenośnika opisane zostało przez niemal 20 wskaźników, reprezentujących charakter i zmiany poboru prądu. Analizie poddano ponad 3700 uruchomień. Na podstawie analizy asocjacji wytypowano grupy reguł opisujących poprawny przebieg pracy przenośnika podczas uruchomienia. Końcowa ocena diagnostyczna polega na obserwacji w sposób ciągły historii uruchomień i porównaniu jakości opisu (dokładności reguł asocjacyjnych na obserwowanych przebiegach) z opisem uzyskanym z dostępnych wcześniej (historycznych, wzorcowych) danych.

17

Formal Framework for Data Mining with Association Rules and Domain Knowledge : Overview of an Approach

Rauch J.

Fundamenta Informaticae

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2015

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Vol. 137, nr 2

171--217

EN

A formal framework for data mining with association rules is introduced. The framework is based on a logical calculus of association rules which is enhanced by several formal tools. The enhancement allows the description of the whole data mining process, including formulation of analytical questions, application of an analytical procedure and interpretation of its results. The role of formalized domain knowledge is discussed.

18

Pozyskiwanie wiedzy z wyników badań wiroprądowych

Frankowski P. K.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2014

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nr 2

45--48

PL

Celem artykułu jest przedstawienie metod pozyskiwania użytecznych wzorców z danych pomiarowych. W artykule opisano metodologię pozyskiwania wiedz umożliwiającą sformułowanie reguł asocjacji, oraz techniki pozwalające na zwiększenie poprawności identyfikacji podstawowych parametrów struktur żelbetowych. Zaprezentowane algorytmy zostały zaimplementowane w nowym, wiroprądowym systemie służącym do identyfikacji grubości otuliny, średnicy oraz właściwości fizycznych prętów zbrojeniowych.

EN

The purpose of this paper is to present methods, dedicated for extract useful patterns from the eddy current measurement data. The paper presents a methodology of knowledge extraction, an association rule learning algorithm and the methods used to improve quality of the data collected by electromagnetic systems. Presented solutions were implemented in the new eddy current system used to evaluation of steel bars in reinforced concrete structures.

19

Apriori-Based Rule Generation in Incomplete Information Databases and Non-Deterministic Information Systems

Sakai H., Wu M., Nakata M.

Fundamenta Informaticae

|

2014

|

Vol. 130, nr 3

343--376

EN

This paper discusses issues related to incomplete information databases and considers a logical framework for rule generation. In our approach, a rule is an implication satisfying specified constraints. The term incomplete information databases covers many types of inexact data, such as non-deterministic information, data with missing values, incomplete information or interval valued data. In the paper, we start by defining certain and possible rules based on non-deterministic information. We use their mathematical properties to solve computational problems related to rule generation. Then, we reconsider the NIS-Apriori algorithm which generates a given implication if and only if it is either a certain rule or a possible rule satisfying the constraints. In this sense, NIS-Apriori is logically sound and complete. In this paper, we pay a special attention to soundness and completeness of the considered algorithmic framework, which is not necessarily obvious when switching from exact to inexact data sets. Moreover, we analyze different types of non-deterministic information corresponding to different types of the underlying attributes, i.e., value sets for qualitative attributes and intervals for quantitative attributes, and we discuss various approaches to construction of descriptors related to particular attributes within the rules' premises. An improved implementation of NIS-Apriori and some demonstrations of an experimental application of our approach to data sets taken from the UCI machine learning repository are also presented. Last but not least, we show simplified proofs of some of our theoretical results.

20

Partial Discharge Pattern Recognition of Medium-Voltage Switchgear Based on Association Rules Mining

Hao Y., Huang C., Zheng W.

Przegląd Elektrotechniczny

|

2013

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R. 89, nr 4

176-179

EN

In the paper, a new partial discharge pattern recognition approach based on association rules mining is developed. Some statistical parameters are extracted from the sampled transient earth voltage data and classic Apriori algorithm is employed to mine the association rules between those parameters and the corresponding fault types. Moreover, using other experimental data obtained in the laboratory, the method is validated and made comparison with two conventional methods.

PL

W artykule opisano metodę wykrywania wyładowań niezupełnych, poprzez wyznaczenie reguł powiązanych z wystąpieniem tych wyładowań i ich wyszukiwanie. Dzięki analizie statystycznej danych dotyczących stanów nieustalonych napięcia oraz zastosowaniu algorytmu apriori, określono zasady powiązane z wystąpieniem zjawisk. Skuteczność działania rozwiązania została potwierdzona wynikami badań eksperymentalnych, które następnie porównano z dwoma konwencjonalnymi metodami.