Mining negative generalized knowledge from relational databases

Yuying, Wu; Chen, Yen-Liang; Chang, Ray-I

doi:10.1016/j.knosys.2010.07.013

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…The algorithm produces a series of k generalized tuples that can be used to describe the general characteristics of k consecutive data segments, where k is specified by users. Wu, Chen, and Chang (2011) noted that the existing AOI methods can extract only positive knowledge from the database. They may miss rare but important negative general knowledge that is unknown, unexpected, or contrary to the user's opinion.…”

Section: Related Workmentioning

confidence: 99%

Mining specific and representative information by the attribute‐oriented induction method

Chen

2020

Expert Systems

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Attribute‐oriented induction (AOI) is a data analysis technique based on induction. The traditional AOI algorithm requires a threshold given by users to determine the number of output tuples. However, it is not easy to set an appropriate tuple threshold, and there is usually noise contained in a dataset. The traditional AOI algorithm can only generate a summary output of a fixed size, but it cannot guarantee that all generalized tuples have sufficient specificity and representativeness. In this article, a new AOI method is proposed to make up for the shortcomings. We introduce the concept of cost to measure the loss of accuracy due to attribute ascension. We also propose two algorithms based on the hierarchical clustering method. By setting cost constraints on each generalized tuple, our method can generate accurate output while eliminating noise, and help users get more informative and clearer results.

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Section: Related Workmentioning

confidence: 99%

Mining specific and representative information by the attribute‐oriented induction method

Chen

2020

Expert Systems

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“…AOI has been applied in many scenarios, such as spatial patterns, medical science, intrusion detection, strategy making, and financial prediction [41,43,13]. However, after reviewing the literature, no reference was found indicating that AOI has been used in scenarios such as Predictive Maintenance.…”

Section: Attribute Oriented Inductionmentioning

confidence: 99%

An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance

Fernandez-Anakabe,

Uriguen,

Ortega

2019

Preprint

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Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this way, it seeks similarities between elements to generate data groupings. AOI was initially conceived as an algorithm for knowledge discovery in databases, but over the years it has been applied to other areas such as spatial patterns, intrusion detection or strategy making. In this paper, AOI has been extended to the field of Predictive Maintenance. The objective is to demonstrate that combining expert knowledge and data collected from the machine can provide good results in the Predictive Maintenance of industrial assets. To this end we adapted the algorithm and used an LSTM approach to perform both the Anomaly Detection (AD) and the Remaining Useful Life (RUL). The results obtained confirm the validity of the proposal, as the methodology was able to detect anomalies, and calculate the RUL until breakage with considerable degree of accuracy.

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“…Attribute oriented induction has a weakness where it only provides a snapshot of the generalized knowledge and not a global picture and global picture in attribute oriented induction can be revealed by trying different thresholds repeatedly. As result by setting different thresholds will obtain different sets of generalized tuples and using different thresholds repeatedly is a time consuming and tedious work (Wu et al 2009). Based on this weakness a novel approach for attribute induction has been proposed where thresholds number as a control for maximum number of tuples of the target class in the final generalized relation will no longer be needed and will be replaced with group by operator in sql select statement.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Logical data model star schema attribute induction Current attribute oriented induction has a weakness where it only provides a snapshot of the generalized knowledge and not a global picture and global picture in current attribute oriented induction can be revealed by trying different thresholds repeatedly (Wu et al 2009). As result by setting different thresholds will obtain different sets of generalized tuples and using different thresholds repeatedly is a time consuming and tedious work (Wu et al 2009). Based on this weaknesses, threshold value as control for maximum number of tuples of target class as learning result is eliminated for star schema attribute induction.…”

Section: Birthplace City Countrymentioning

confidence: 99%

Attribute oriented induction with star schema

H¹

2010

IJDMS

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: This paper will propose a novel star schema attribute induction as a new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using non rule based concept hierarchy by implementing both of approaches. In novel star schema attribute induction some improvements have been implemented like elimination threshold number as maximum tuples control for generalization result, there is no ANY as the most general concept, replacement the role concept hierarchy with concept tree, simplification for the generalization strategy steps and elimination attribute oriented induction algorithm. Novel star schema attribute induction is more powerful than the current attribute oriented induction since can produce small number final generalization tuples and there is no ANY in the results.

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Mining negative generalized knowledge from relational databases

Cited by 10 publications

References 26 publications

Mining specific and representative information by the attribute‐oriented induction method

Mining specific and representative information by the attribute‐oriented induction method

An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance

Attribute oriented induction with star schema

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