scholarly journals Association Rules over Time

2021 ◽  
pp. 1-16
Author(s):  
Iztok Fister ◽  
Iztok Fister
Keyword(s):  
Association Rules ◽  
Over Time

Finding Minimal Infrequent Elements in Multi-Dimensional Data Defined over Partially Ordered Sets and its Applications

2010 ◽  
pp. 98-116
Author(s):  
Khaled M. Elbassioni
Keyword(s):  
Association Rules ◽  
Partially Ordered Set ◽  
Quantitative Data ◽  
Partially Ordered Sets ◽  
Threshold Value ◽  
Ordered Sets ◽  
Time Intervals ◽  
Partially Ordered ◽  
The Given ◽  
Over Time

The authors consider databases in which each attribute takes values from a partially ordered set (poset). This allows one to model a number of interesting scenarios arising in different applications, including quantitative databases, taxonomies, and databases in which each attribute is an interval representing the duration of a certain event occurring over time. A natural problem that arises in such circumstances is the following: given a database D and a threshold value t, find all collections of “generalizations” of attributes which are “supported” by less than t transactions from D. They call such collections infrequent elements. Due to monotonicity, they can reduce the output size by considering only minimal infrequent elements. We study the complexity of finding all minimal infrequent elements for some interesting classes of posets. The authors show how this problem can be applied to mining association rules in different types of databases, and to finding “sparse regions” or “holes” in quantitative data or in databases recording the time intervals during which a re-occurring event appears over time. Their main focus will be on these applications rather than on the correctness or analysis of the given algorithms.

Visualizing constraint-based temporal association rules

2001 ◽  
Vol 15 (5) ◽  
pp. 401-410 ◽  
Author(s):  
PANU KORPIPÄÄ
Keyword(s):  
Association Rules ◽  
Three Dimensional ◽  
Temporal Association ◽  
Hot Strip Mill ◽  
Virtual Model ◽  
Rule Mining ◽  
Continuous Processes ◽  
Hot Strip ◽  
Mining Tool ◽  
Over Time

When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and glyphs. Interactive functions used for displaying rule attributes and exploring temporal rules are implemented by utilizing Virtual Reality Modeling Language v2 mechanisms. Furthermore, to give a direction of rule potential for the user, the rule statistical interestingness is evaluated on the basis of combining weighted characteristics of rule and rule matrix. A constraint-based association rule mining tool which creates the virtual model as an output is presented, including the most relevant experiences from the development of the tool. The applicability of the overall approach has been verified by using the developed tool for data mining on a hot strip mill of a steel plant.

Mining Generalized Association Rules in an Evolving Environment

2011 ◽  
pp. 1268-1274
Author(s):  
Wen-Yang Lin ◽  
Ming-Cheng Tseng
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
State Of The Art ◽  
Dynamic Environment ◽  
Static Data ◽  
Generalized Association Rules ◽  
Static Item ◽  
Data Source ◽  
Over Time ◽  
Art Techniques

The mining of Generalized Association Rules (GARs) from a large transactional database in the presence of item taxonomy has been recognized as an important model for data mining. Most previous studies on mining generalized association rules, however, were conducted on the assumption of a static environment, i.e., static data source and static item taxonomy, disregarding the fact that the taxonomy might be updated as new transactions are added into the database over time, and as such, the analysts may have to continuously change the support and confidence constraints, or to adjust the taxonomies from different viewpoints to discover more informative rules. In this chapter, we consider the problem of mining generalized association rules in such a dynamic environment. We survey different strategies incorporating state-of-the-art techniques for dealing with this problem and investigate how to efficiently update the discovered association rules when there are transaction updates to the database along with item taxonomy evolution and refinement of support constraint.

scholarly journals Why IEEE Xplore Matters for Research Trend Analysis in the Energy Sector

2021 ◽  
pp. 44-58
Author(s):  
B. Chigarev
Keyword(s):  
Growth Rate ◽  
Conference Proceedings ◽  
Association Rules ◽  
Web Of Science ◽  
Research Trends ◽  
Research Trend ◽  
Bibliometric Data ◽  
Author Keywords ◽  
Key Terms ◽  
Over Time

The paper aims to briefly compare and analyze the results of queries to IEEE Xplore and the leading abstract databases Scopus and Web of Science to identify research trends. Some errors were revealed in the Author Keywords in Web of Science. Therefore, a more detailed analysis that involved comparing various types of key terms was made only for IEEE Xplore and Scopus platforms. The study employed IEEE Access journal metadata as indexed on both platforms. Sample matching for IEEE Xplore and Scopus was achieved by comparing DOI. The IEEE Xplore metadata contains more key term types, which provides an advantage in analyzing research trends. Using NSPEC Controlled Terms from expert-compiled vocabulary provides more stable data, which gives an advantage when considering the change of terms over time. Apriori, an algorithm for finding association rules, was used to compare the co-occurrence of the terms for a more detailed description of sample subjects on both platforms. VOSviewer was used to analyze trends in scientific research based on IEEE Xplore data. The 2011-2021 ten-year period was divided into two sub-intervals for comparing the occurrence of Author Keywords, IEEE Terms, and NSPEC Controlled Terms. Bibliometric data of the IEEE conference proceedings was used to illustrate the importance of context in estimating the growth rate of publishing activity on a topic of interest.

scholarly journals Association rules mining applied in the animal movement exploratory analysis

2019 ◽  
Author(s):  
S. G. Fontes ◽  
P. L. P. Côrrea ◽  
S. L. Stanzani ◽  
R. G. Morato
Keyword(s):  
Association Rules ◽  
Animal Behavior ◽  
Animal Movement ◽  
Movement Analysis ◽  
Cebus Capucinus ◽  
Exploratory Analysis ◽  
Spatiotemporal Data ◽  
Association Rules Mining ◽  
Movement Data ◽  
Over Time

The animal movement analysis determines the animal behavior, which is the basis for understanding the interaction between species and the environment and to guide actions of preservation and conservation. The challenge is how to explore this movement data, getting indications about how the animal behaves over time and space. In this sense, a framework to animal movement exploratory analysis is presented, that combines algorithms for spatiotemporal data analysis and association rules mining, as a first step to answer questions related to animal behavior. We performed the framework’s evaluation in the exploratory analysis of monitored monkeys (Cebus capucinus) in the Panamá.

Mining Rare Association Rules by Discovering Quasi-Functional Dependencies

2010 ◽  
pp. 131-149
Author(s):  
Giulia Bruno ◽  
Paolo Garza ◽  
Elisa Quintarelli
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Incremental Algorithm ◽  
Functional Dependencies ◽  
Data Mining Technique ◽  
Data Set ◽  
Rare Association ◽  
Normal Behavior ◽  
The Impact ◽  
Over Time

In the context of anomaly detection, the data mining technique of extracting association rules can be used to identify rare rules which represent infrequent situations. A method to detect rare rules is to first infer the normal behavior of objects in the form of quasi-functional dependencies (i.e. functional dependencies that frequently hold), and then analyzing rare violations with respect to them. The quasi-functional dependencies are usually inferred from the current instance of a database. However, in several applications, the database is not static, but new data are added or deleted continuously. Thus, the anomalies have to be updated because they change over time. In this chapter, we propose an incremental algorithm to efficiently maintain up-to-date rules (i.e., functional and quasi-functional dependencies). The impact of the cardinality of the data set and the number of new tuples on the execution time is evaluated through a set of experiments on synthetic and real databases, whose results are here reported.

Social transmission bias and the cultural evolution of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
David Hirshleifer ◽  
Siew Hong Teoh
Keyword(s):  
Cultural Evolution ◽  
Social Transmission ◽  
Cognitive Approach ◽  
Economic Problems ◽  
Economic Attitudes ◽  
The Social ◽  
Economic Beliefs ◽  
Over Time

AbstractEvolved dispositions influence, but do not determine, how people think about economic problems. The evolutionary cognitive approach offers important insights but underweights the social transmission of ideas as a level of explanation. The need for asocialexplanation for the evolution of economic attitudes is evidenced, for example, by immense variations in folk-economic beliefs over time and across individuals.

Articulatory Generalization in Two Word-Medial, Ambisyllabic Contexts

1988 ◽  
Vol 19 (3) ◽  
pp. 251-258 ◽  
Author(s):  
Virginia I. Wolfe ◽  
Suzanne D. Blocker ◽  
Norma J. Prater
Keyword(s):  
Common View ◽  
Perceptual Salience ◽  
The Common ◽  
Unitary Concept ◽  
Over Time

Articulatory generalization of velar cognates /k/, /g/ in two phonologically disordered children was studied over time as a function of sequential word-morpheme position training. Although patterns of contextual acquisition differed, correct responses to the word-medial, inflected context (e.g., "picking," "hugging") occurred earlier and exceeded those to the word-medial, noninflected context (e.g., "bacon," "wagon"). This finding indicates that the common view of the word-medial position as a unitary concept is an oversimplification. Possible explanations for superior generalization to the word-medial, inflected position are discussed in terms of coarticulation, perceptual salience, and the representational integrity of the word.

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