Action Rules

Author(s):  
Zbigniew W. Ras ◽  
Angelina Tzacheva ◽  
Li-Shiang Tsay

There are two aspects of interestingness of rules that have been studied in data mining literature, objective and subjective measures (Liu, 1997; Adomavicius & Tuzhilin, 1997; Silberschatz & Tuzhilin, 1995, 1996). Objective measures are data-driven and domain-independent. Generally, they evaluate the rules based on their quality and similarity between them. Subjective measures, including unexpectedness, novelty and actionability, are user-driven and domain-dependent.

Author(s):  
Zbigniew W. Ras ◽  
Elzbieta Wyrzykowska ◽  
Li-Shiang Tsay

There are two aspects of interestingness of rules that have been studied in data mining literature, objective and subjective measures (Liu et al., 1997), (Adomavicius & Tuzhilin, 1997), (Silberschatz & Tuzhilin, 1995, 1996). Objective measures are data-driven and domain-independent. Generally, they evaluate the rules based on their quality and similarity between them. Subjective measures, including unexpectedness, novelty and actionability, are user-driven and domaindependent. A rule is actionable if user can do an action to his/her advantage based on this rule (Liu et al., 1997). This definition, in spite of its importance, is too vague and it leaves open door to a number of different interpretations of actionability. In order to narrow it down, a new class of rules (called action rules) constructed from certain pairs of association rules, has been proposed in (Ras & Wieczorkowska, 2000). Interventions introduced in (Greco et al., 2006) and the concept of information changes proposed in (Skowron & Synak, 2006) are conceptually very similar to action rules. Action rules have been investigated further in (Wang at al., 2002), (Tsay & Ras, 2005, 2006), (Tzacheva & Ras, 2005), (He at al., 2005), (Ras & Dardzinska, 2006), (Dardzinska & Ras, 2006), (Ras & Wyrzykowska, 2007). To give an example justifying the need of action rules, let us assume that a number of customers have closed their accounts at one of the banks. We construct, possibly the simplest, description of that group of people and next search for a new description, similar to the one we have, with a goal to identify a new group of customers from which no-one left that bank. If these descriptions have a form of rules, then they can be seen as actionable rules. Now, by comparing these two descriptions, we may find the cause why these accounts have been closed and formulate an action which if undertaken by the bank, may prevent other customers from closing their accounts. Such actions are stimulated by action rules and they are seen as precise hints for actionability of rules. For example, an action rule may say that by inviting people from a certain group of customers for a glass of wine by a bank, it is guaranteed that these customers will not close their accounts and they do not move to another bank. Sending invitations by regular mail to all these customers or inviting them personally by giving them a call are examples of an action associated with that action rule.


Author(s):  
Solange Oliveira Rezende ◽  
Edson Augusto Melanda ◽  
Magaly Lika Fujimoto ◽  
Roberta Akemi Sinoara ◽  
Veronica Oliveira de Carvalho

Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that can be generated, the knowledge post-processing phase becomes very complex and challenging. There are several evaluation measures that can be used in this phase to assist users in finding interesting rules. These measures, which can be divided into data-driven (or objective measures) and user-driven (or subjective measures), are first discussed and then analyzed for their pros and cons. A new methodology that combines them, aiming to use the advantages of each kind of measure and to make user’s participation easier, is presented. In this way, data-driven measures can be used to select some potentially interesting rules for the user’s evaluation. These rules and the knowledge obtained during the evaluation can be used to calculate user-driven measures, which are used to aid the user in identifying interesting rules. In order to identify interesting rules that use our methodology, an approach is described, as well as an exploratory environment and a case study to show that the proposed methodology is feasible. Interesting results were obtained. In the end of the chapter tendencies related to the subject are discussed.


Author(s):  
Arun Thotapalli Sundararaman

Data Quality (DQ) in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in Business Intelligence (BI) applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI System has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of DQ definition and measurement for data mining for BI, analyzes the gaps therein, besides reviewing proposed solutions and providing a direction for future research and practice in this area.


2005 ◽  
Vol 20 (1) ◽  
pp. 39-61 ◽  
Author(s):  
KEN MCGARRY

It is a well-known fact that the data mining process can generate many hundreds and often thousands of patterns from data. The task for the data miner then becomes one of determining the most useful patterns from those that are trivial or are already well known to the organization. It is therefore necessary to filter out those patterns through the use of some measure of the patterns actual worth. This article presents a review of the available literature on the various measures devised for evaluating and ranking the discovered patterns produced by the data mining process. These so-called interestingness measures are generally divided into two categories: objective measures based on the statistical strengths or properties of the discovered patterns and subjective measures that are derived from the user's beliefs or expectations of their particular problem domain. We evaluate the strengths and weaknesses of the various interestingness measures with respect to the level of user integration within the discovery process.


1979 ◽  
Vol 7 (6) ◽  
pp. 583-587 ◽  
Author(s):  
Thomas Roth ◽  
Elizabeth I Tietz ◽  
Milton Kramer ◽  
Mark Kaffeman

The present study evaluated the efficacy of 25 mg of quazepam, a new benzodiazepine hypnotic, in a population of chronic insomniacs. The results indicate that a single dose (25 mg) administered for one night was efficacious when measured both objectively by polysomnographic recording and subjectively by questionnaire with no reported side-effects. The change in the objective measures paralleled the direction of change in subjective measures. Sleep efficiency and sleep maintenance were improved without EEG changes in Stages 2, 3-4, and REM. Further study is needed to evaluate the effects of chronic administration of different doses of quazepam in chronic insomniacs.


2017 ◽  
Vol 3 (2) ◽  
pp. 735-738
Author(s):  
Wolfgang Doneit ◽  
Jana Lohse ◽  
Kristina Glesing ◽  
Clarissa Simon ◽  
Monika Fischer ◽  
...  

AbstractIn the project I-CARE a technical system for tablet devices is developed that captures the personal needs and skills of people with dementia. The system provides activation content such as music videos, biographical photographs and quizzes on various topics of interest to people with dementia, their families and professional caregivers. To adapt the system, the activation content is adjusted to the daily condition of individual users. For this purpose, emotions are automatically detected through facial expressions, motion, and voice. The daily interactions of the users with the tablet devices are documented in log files which can be merged into an event list. In this paper, we propose an advanced format for event lists and a data analysis strategy. A transformation scheme is developed in order to obtain datasets with features and time series for popular methods of data mining. The proposed methods are applied to analysing the interactions of people with dementia with the I-CARE tablet device. We show how the new format of event lists and the innovative transformation scheme can be used to compress the stored data, to identify groups of users, and to model changes of user behaviour. As the I-CARE user studies are still ongoing, simulated benchmark log files are applied to illustrate the data mining strategy. We discuss possible solutions to challenges that appear in the context of I-CARE and that are relevant to a broad range of applications.


2021 ◽  
Vol 27 (2) ◽  
pp. 69-76
Author(s):  
Devan Sedlacek ◽  
Matthew Beacom ◽  
Sabin R. Bista ◽  
Risto Rautiainen ◽  
Ka-Chun Siu

HighlightsThe farming population is at risk of injury due to sleep deprivation.Loss of sleep during previous night affects balance performance in farmers.Objective measures of sleep are more reliable than subjective measures for predicting balance performance.Abstract. This study aimed to investigate the ability of both subjective and objective sleep measures to predict balance difficulty in agricultural workers. Seven male farmers from rural Nebraska were analyzed for static balance performance following a bout of sleep. Actiwatches were used to measure objective sleep hours and subjective questionnaires, including the Epworth Sleepiness Scale and the Pittsburgh Sleep Quality Index, were used to measure subjective hours of sleep and sleep quality. The participants were observed for 12 sessions, with six in planting season and six in harvest season. Static balance testing consisted of measuring the area, total displacement, and maximum range in the anteroposterior and mediolateral directions of the individual’s center of pressure with Tekscan pressure mats. Overall, it was found that objective measures had a higher correlation with the magnitude of balance deviations than subjective measures. Keywords: Actiwatch, Agricultural worker, Injury, Sleep deprivation.


2015 ◽  
Vol 639 ◽  
pp. 21-30 ◽  
Author(s):  
Stephan Purr ◽  
Josef Meinhardt ◽  
Arnulf Lipp ◽  
Axel Werner ◽  
Martin Ostermair ◽  
...  

Data-driven quality evaluation in the stamping process of car body parts is quite promising because dependencies in the process have not yet been sufficiently researched. However, the application of data mining methods for the process in stamping plants would require a large number of sample data sets. Today, acquiring these data represents a major challenge, because the necessary data are inadequately measured, recorded or stored. Thus, the preconditions for the sample data acquisition must first be created before being able to investigate any correlations. In addition, the process conditions change over time due to wear mechanisms. Therefore, the results do not remain valid and a constant data acquisition is required. In this publication, the current situation in stamping plants regarding the process robustness will be first discussed and the need for data-driven methods will be shown. Subsequently, the state of technology regarding the possibility of collecting the sample data sets for quality analysis in producing car body parts will be researched. At the end of this work, an overview will be provided concerning how this data collection was implemented at BMW as well as what kind of potential can be expected.


2016 ◽  
Vol 38 (2) ◽  
pp. 457-475 ◽  
Author(s):  
JUAN HARO ◽  
PILAR FERRÉ ◽  
ROGER BOADA ◽  
JOSEP DEMESTRE

ABSTRACTThis study presents semantic ambiguity norms for 530 Spanish words. Two subjective measures of semantic ambiguity and two subjective measures of relatedness of ambiguous word meanings were collected. In addition, two objective measures of semantic ambiguity were included. Furthermore, subjective ratings were obtained for some relevant lexicosemantic variables, such as concreteness, familiarity, emotional valence, arousal, and age of acquisition. In sum, the database overcomes some of the limitations of the published databases of Spanish ambiguous words; in particular, the scarcity of measures of ambiguity, the lack of relatedness of ambiguous word meanings measures, and the absence of a set of unambiguous words. Thus, it will be very helpful for researchers interested in exploring semantic ambiguity as well as for those using semantic ambiguous words to study language processing in clinical populations.


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