Multidimensional Association Research Articles

Method based on rough set for mining multi-dimensional association rules

It is very time-consuming to discover association rules from the mass of data,and not all the rules are attractive to the user,so a lot of irrelevant information to the user's requirements may be generated when traditional mining methods are applied.In addition,most of the existing algorithms are for discovering one-dimensional association rules.Therefore,the authors defined a mining language which allowed users to specify items of interest to the association rules,as well as the parameters(for example,support,confidence,etc.).A method based on rough set theory for multi-dimensional association rules mining was also proposed,which dynamically generated frequent item sets and multi-dimensional association rules,and reduced the search space to generate frequent item sets.Finally,an example verifies the feasibility and effectiveness of the method.

Association Rule Mining from an Intelligent Tutor

Educational data mining is a very novel research area, offering fertile ground for many interesting data mining applications. Educational data mining can extract useful information from educational activities for better understanding and assessment of the student learning process. In this way, it is possible to explore how students learn topics in interactive learning environments such as an intelligent tutoring system (ITS). In this article, we demonstrate the use of association rule mining to extract mistakes often occurring together in the student data captured in an ITS we developed, called “intelligent tutor for computer systems course” (ITCS). Student assessment results from the ITCS were analyzed using association rule mining. This analytical process could help teachers to carry out modification to the ITCS to improve it together with the concept and sub-concept relationships obtained. We further developed two software programs to extract hidden patterns from the student assessment results on the ITCS using association rule mining. The first program analyzes and finds association rules derived from the students' incorrect answers to the concepts by single dimensional association rule mining, while the second program does so by multidimensional association rule mining. Design of these programs and the data mining results in this study are described.

Granule mining oriented data warehousing model for representations of multidimensional association rules

To promise the quality of multidimensional association mining in real applications is a challenging research issue. The challenging issue is how to represent multidimensional association rules efficiently because of the complicated correlation between attributes. Multi-tier granule mining is one initiative in solving this challenge. This paper presents a granule mining oriented data warehousing model. It can divide attributes into tiers and discover granules for each tier from large multidimensional databases. In addition, it uses association mappings to generate association rules for describing the correlation between tiers. Experiments for the proposed model and the testing results are prosecuted.

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the confusion matrix that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.

Mining fuzzy association rules in a bank-account database

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers.

Analysis of multivariate frequency data by graphical models and generalizations of the multidimensional row-column association model.

Models used to analyze cross-classifications of counts from psychological experiments must represent associations between multiple discrete variables and take into account attributes of stimuli, experimental conditions, or characteristics of subjects. The models must also lend themselves to psychological interpretations about underlying structures mediating the relationship between stimuli and responses. To meet these needs, the author extends the graphical latent variable models for nominal and/or ordinal data proposed by C. J. Anderson and J. K. Vermunt (2000) to situations in which dependencies between observed variables are not fully accounted for by the latent variables. The graphical models provide a unified framework for studying multivariate associations that include log-linear models and log-multiplicative association models as special cases.

Analysis of multivariate frequency data by graphical models and generalizations of the multidimensional row-column association model.

Analysis of multivariate frequency data by graphical models and generalizations of the multidimensional row-column association model.

Generalized multidimensional association rules

The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large-scale databases. And there has been a spurt of research activities around this problem. Traditional association rule mining is limited to intra-transaction. Only recently the concept ofN-dimensional inter-transaction association rule (NDITAR) was proposed by H.J. Lu. This paper modifies and extends Lu’s definition of NDITAR based on the analysis of its limitations, and the generalized multidimensional association rule (GMDAR) is subsequently introduced, which is more general, flexible and reasonable than NDITAR.

Exploration of Ordinal Data Using Association Rules

The discovery of association rules is a very efficient data mining technique that is especially suitable for large amounts of categorical data. This paper shows how the discovery of association rules can be of benefit for numeric data as well. Based on a review of previous approaches we introduce Q2, a faster algorithm for the discovery of multi-dimensional association rules over ordinal data. We experimentally compare the new algorithm with the previous approach, obtaining performance improvements of more than an order of magnitude on supermarket data. In addition, a new absolute measure for the interestingness of quantitative association rules is introduced. It is based on the view that quantitative association rules have to be interpreted with respect to their Boolean generalizations. This measure has two major benefits compared to the previously used relative interestingness measure; first, it speeds up rule extraction and evaluation and second, it is easier to interpret for a user. Finally we introduce a rule browser which supports the exploration of ordinal data with quantitative association rules.

Bottom-up computation of sparse and Iceberg CUBE

We introduce the Iceberg-CUBE problem as a reformulation of the datacube (CUBE) problem. The Iceberg-CUBE problem is to compute only those group-by partitions with an aggregate value (e.g., count) above some minimum support threshold. The result of Iceberg-CUBE can be used (1) to answer group-by queries with a clause such as HAVING COUNT(*) >= X, where X is greater than the threshold, (2) for mining multidimensional association rules, and (3) to complement existing strategies for identifying interesting subsets of the CUBE for precomputation. We present a new algorithm (BUC) for Iceberg-CUBE computation. BUC builds the CUBE bottom-up; i.e., it builds the CUBE by starting from a group-by on a single attribute, then a group-by on a pair of attributes, then a group-by on three attributes, and so on. This is the opposite of all techniques proposed earlier for computing the CUBE, and has an important practical advantage: BUC avoids computing the larger group-bys that do not meet minimum support. The pruning in BUC is similar to the pruning in the Apriori algorithm for association rules, except that BUC trades some pruning for locality of reference and reduced memory requirements. BUC uses the same pruning strategy when computing sparse, complete CUBEs. We present a thorough performance evaluation over a broad range of workloads. Our evaluation demonstrates that (in contrast to earlier assumptions) minimizing the aggregations or the number of sorts is not the most important aspect of the sparse CUBE problem. The pruning in BUC, combined with an efficient sort method, enables BUC to outperform all previous algorithms for sparse CUBEs, even for computing entire CUBEs, and to dramatically improve Iceberg-CUBE computation.

Natural History of Prostatism: Impaired Health States in Men With Lower Urinary Tract Symptoms

Purpose Lower urinary tract symptoms are reported to have a significant impact on quality of life. However, the impact on specific aspects of health status is not clear. We evaluated the association between lower urinary tract symptoms, and physical and mental aspects of health using community based data from a cross-sectional component of a prospective cohort study. Materials and Methods A total of 2,133 men 41 to 84 years old who were randomly selected from the Olmsted County, Minnesota population completed the 36-item health status questionnaire and a previously validated questionnaire assessing urinary symptoms. Eight domains, measured on a scale of 0 to 100, were used to assess general health status. Men were classified as having an impaired health status if they scored less than 75 points on the scale. Symptom severity (none, mild, moderate or severe) was measured from responses to the urinary symptom questionnaire. Results The results demonstrated a cross-sectional decrease in mean health status scores for all 8 domains across levels of increasing urinary symptom severity. The strongest associations between health status scores and urinary symptoms (severe versus none) were observed for role limitation due to physical problems (odds ratio 15.7, 95% confidence interval 6.6 to 37.0), energy/fatigue (odds ratio 9.2, 95% confidence interval 4.7 to 18.1), role limitation due to emotional problems (odds ratio 8.7, 95% confidence interval 4.1 to 18.2) and general perception of health (odds ratio 7.2, 95% confidence interval 3.8 to 13.4). For these 4 dimensions men with mild urinary symptoms were also significantly more likely to have an impaired health status. Adjustment for age and co-morbidity did not alter the results. Conclusions These findings suggest that urinary symptoms have a multidimensional association with physical and mental aspects of health. Although lower urinary tract symptoms may be the cause of an impaired health status, men with impaired health conditions may be more sensitive to prevalent urinary symptoms and more likely to report them. While the casual nature of this association has not been ascertained, these results may help to identify appropriate health dimensions to assess in patients with lower urinary tract symptoms.

Autism, amnesia, hippocampus, and learning

Autism is held to be the result of the failure of a central cognitive processor which is necessary for flexible multidimensional association of sensorial stimuli, memory, and motivational states. Failure of this processor produces rigid, invariant, rote behavior, thought and language and aberrant modulation of emotion. It is argued that this central processing function is critically dependent on the hippocampus. Thus autism is postulated to be the developmental syndrome of hippocampal dysfunction. The hippocampus is postulated to be necessary for normal development in the child of language syntax, semantics, and pragmatics; the capacity for creativity and generativity in language and behavior, and combinatorial possibilities in general; for the integration of motivational states with experience and learning; and for the construction of a complex, useful and flexible structure of meaning. These constructs may become independent of hippocampus for use, but hippocampus is still required to modify or add to them. Finally, this analysis suggests a specific hypothesis of hippocampal organization which I advance as an hypothesis: that the hippocampus can be modelled as a multidimensional system in which the unique intersection of all input dimensions is the resultant.