An Adaptive Method of Numerical Attribute Merging for Quantitative Association Rule Mining

Many algorithms have been proposed for mining of boolean association rules. However, very little work has been done in mining quantitative association rules. Although we can transform quantitative attributes into boolean attributes, this approach is not effective and is difficult to scale up for high dimensional case and also may result in many imprecise association rules. Newly designed algorithms for quantitative association rules still are persecuted by nonscalable and noise problem. In this paper, an efficient algorithm, QAR-miner, is proposed. By using the notion of “density” to capture the characteristics of quantitative attributes and an efficient procedure to locate the “dense regions”, QAR-miner not only can solve the problems of previous approaches, but also can scale up well for high dimensional case. Evaluations on QAR-miner have been performed using both synthetic and real databases. Preliminary results show that QAR-miner is effective and can scale up quite linearly with the increasing number of attributes.KeywordsAssociation RuleDense RegionMining Association RuleSupport ThresholdDensity ThresholdThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.

Nowadays many applications generate mixed data objects consisting of numerical and categorical attributes. Simultaneously dealing with mixed objects is more challenging and various approaches convert one type to another one to face this issue. But in many cases this leads to information loss. Therefore integrating categorical and numerical attributes sounds reasonable since it keeps the original format of any attribute. In this paper we focus on clustering and especially density-based clustering as one of the well-known clustering approaches well-performed on arbitrary shape clusters. Density-based clustering algorithms require a distance measure to discover dense regions. Therefore we introduce the distance hierarchy as a distance measure appropriate for both categorical and numerical attributes. However setting the parameters regarding any parametric clustering algorithm could be another issue. Therefore we employ minimum description length principle to automate this process.

Algorithms for mining Boolean association rules have been well studied and documented, but they cannot deal with quantitative data directly. In this paper, a novel algorithm MQAR (Mining Quantitative Association Rules based on dense grid) which uses tree structure DGFP-tree to cluster dense subspaces is proposed, which transforms mining quantitative association rules into finding dense regions. MQAR not only can solve the conflict between minimum support problem and minimum confidence problem, but also can find the interesting quantitative association rules which may be missed by previous algorithms. Experimental results show that MQAR can efficiently find quantitative association rules.

Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

In the task of extracting association rules in datasets with numerical attributes, the usual approach is to previously discretize those attributes. In this paper, a deterministic method for the extraction of quantitative association rules is presented, which does not employ a previous discretization. It also tries to eliminate redundant rules and reduce the search. Experiments have been performed comparing it with the well-known deterministic algorithm Apriori, and statistical validation of the results was carried out using nonparametric tests. From the results obtained, the proposed approach can be seen as a suitable deterministic way of extracting quantitative association rules without a previous discretization.

Association rule mining among frequent items has been extensively studied in data mining research. However, in the recent years, there is an increasing demand of mining the infrequent items (such as rare but expensive items). Since exploring interesting relationship among infrequent items has not been discussed much in the literature, in this paper, we propose two simple, practical and effective schemes to mine association rules among rare items. Our algorithm can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. Our schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, we explore quantitative association rule mining in transactional database among infrequent items by associating quantities of items purchased; some interesting examples are drawn to illustrate the significance of such mining.

The most challenging issues in association rule mining are dealing with numerical attributes and accommodating several criteria to discover optimal rules without any preprocessing activities or predefined parameter values. In order to deal with these problems, this paper proposes a multi-objective particle swarm optimization using an adaptive archive grid based on Pareto optimal strategy for numerical association rule mining. The proposed method aims to optimize confidence, comprehensibility and interestingness for rule discovery. By implementing this method, the numerical association rule does not require any major preprocessing activities such as discretization. Moreover, minimum support and confidence are not prerequisites. The proposed method is evaluated using three benchmark datasets containing numerical attributes. Furthermore, it is applied to a real case dataset taken from a weight loss application in order to discover association rules in terms of the behavior of customer page usage.

Mixed feature selection based on granulation and approximation

A database can consist of numerical and non-numerical attributes. However, several data processing algorithms, such as K-means clustering, can be used only in a dataset with numerical attributes. Data generalization by using Naïve Bayes and K-means clustering methods is usually employed WEKA (Waikato environment for knowledge analysis) application. Although the strength of WEKA lies in increasingly complete and sophisticated algorithms, the success of data mining still lies in the knowledge factor of the human implementer. The task of collecting high-quality data and knowledge of modeling and the use of appropriate algorithms is needed to guarantee the accuracy of the expected formulations. In this paper, we propose a simple web-based application that can be used like WEKA. The methodology used in this study includes several stages. The first stage is the preparation of data, which is the tic-tac-toe game dataset that is converted to CSV (comma-separated values) format. The next stage is the process of modifying data from non-numeric to numeric, specifically for clustering with the K-means algorithm. Afterward, the calculation of the distance between data is conducted and followed by data clustering. The final stage is the summary of these processes and results. From the experimental results, it was found that clustering can be done on categorical attributes that are transformed first into the numerical form using web-based applications.

This paper proposes to exploit heterogeneous data, i.e. data described by both numerical and categorical features, so as to gain knowledge about the categorical attributes from the numerical ones. More precisely, it aims at discovering whether, according to a given data set, based on information provided by the numerical attributes, some categorical attributes actually are ordinal ones and, additionally, at establishing ranking relations between the category values. To that aim, the paper proposes the 3-step methodology OSACA, standing for Order Seeking Algorithm for Categorical Attributes: it first consists in extracting gradual patterns from the numerical attributes, to identify rich ranking information about the data; it then applies mathematical morphology tools, more precisely alternated filters, to induce an associated order on the categorical attributes. The third step evaluates the quality of the candidate rankings through an original measure derived from the rank entropy discrimination.

Association rules are useful for determining correlations between attributes of a relation and have applications in the marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. Optimized association rules are permitted to contain uninstantiated attributes and the problem is to determine instantiations such that either the support, confidence, or gain of the rule is maximized. In this paper, we generalize the optimized gain association rule problem by permitting rules to contain disjunctions over uninstantiated numeric attributes. Our generalized association rules enable us to extract more useful information about seasonal and local patterns involving the uninstantiated attribute. For rules containing a single numeric attribute, we present an algorithm with linear complexity for computing optimized gain rules. Furthermore, we propose a bucketing technique that can result in a significant reduction in input size by coalescing contiguous values without sacrificing optimality. We also present an approximation algorithm based on dynamic programming for two numeric attributes. Using recent results on binary space partitioning trees, we show that the approximations are within a constant factor of the optimal optimized gain rules. Our experimental results with synthetic data sets for a single numeric attribute demonstrate that our algorithm scales up linearly with the attribute's domain size as well as the number of disjunctions. In addition, we show that applying our optimized rule framework to a population survey real-life data set enables us to discover interesting underlying correlations among the attributes.

As geospatial data grows explosively, there is a great demand for the incorporation of data mining techniques into a geospatial context. Association rules mining is a core technique in data mining and is a solid candidate for the associative analysis of large geospatial databases. In this article, we propose a geospatial knowledge discovery framework for automating the detection of multivariate associations based on a given areal base map. We investigate a series of geospatial preprocessing steps involving data conversion and classification so that the traditional Boolean and quantitative association rules mining can be applied. Our framework has been integrated into GISs using a dynamic link library to allow the automation of both the preprocessing and data mining phases to provide greater ease of use for users. Experiments with real-crime datasets quickly reveal interesting frequent patterns and multivariate associations, which demonstrate the robustness and efficiency of our approach.

Most clustering algorithms have been designed only for pure numerical or pure categorical data sets, while nowadays many applications generate mixed data. It raises the question how to integrate various types of attributes so that one could efficiently group objects without loss of information. It is already well understood that a simple conversion of categorical attributes into a numerical domain is not sufficient since relationships between values such as a certain order are artificially introduced. Leveraging the natural conceptual hierarchy among categorical information, concept trees summarize the categorical attributes. In this paper, we introduce the algorithm ClicoT (CLustering mixed-type data Including COncept Trees) as reported by Behzadi et al. (Advances in Knowledge Discovery and Data Mining, Springer, Cham, 2019) which is based on the minimum description length principle. Profiting of the conceptual hierarchies, ClicoT integrates categorical and numerical attributes by means of a MDL-based objective function. The result of ClicoT is well interpretable since concept trees provide insights into categorical data. Extensive experiments on synthetic and real data sets illustrate that ClicoT is noise-robust and yields well-interpretable results in a short runtime. Moreover, we investigate the impact of concept hierarchies as well as various data characteristics in this paper.

Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications may contain multiple numerical sensitive attributes. Directly applying the existing single-numerical-sensitive-attribute and multiple categorical-sensitive-attributes privacy preserving techniques often causes unexpected private information disclosure. They are particularly prone to the proximity breach, a privacy threat specific to numerical sensitive attributes in data publication. In this paper we propose a privacy-preserving data publishing method, namely MNSACM, that uses the ideas of clustering and Multi-Sensitive Bucketization (MSB) to publish microdata with multiple numerical sensitive attributes. Through an example we show the effectiveness of this method in privacy protection tomultiple numerical sensitive attributes.