Maximal Association Rules Research Articles

Maximal association analysis using logical formulas over soft sets

Discovering interesting and useful association rules from the collected data is an issue of great importance in pattern mining. Although a myriad of association rules can be extracted with traditional rule mining techniques, some of the obtained rules might be redundant or even meaningless in many cases. To overcome this difficulty, logical formulas over soft sets are applied to maximal association mining in this study. With the help of logical formulas over soft sets, all critical concepts for mining both regular and maximal association rules are incorporated into a common framework, and uniform mathematical characterizations of these concepts are provided accordingly. Three algorithms are also designed to develop a new approach to maximal association rule mining using logical formulas over soft sets. Moreover, we present two examples to show theoretical value of the obtained results and practical applicability of the proposed approach. The first example relies on a clinical diagnosis data set and illustrates the advantages of applying logical formula over soft sets to rule extraction. In the second example, we conduct a case study based on a data set regarding Nobel Laureates to show that the proposed approach is helpful for discovering interesting facts in real-world scenarios.

Discovering the Association of Algae with Physicochemical Variables in Erhai Lake

Erhai Lake is the seventh largest freshwater lake in China where has been encountered with water pollution by the algae. One of important indicator of the water quality is the density of algae in the water, and the growth rate of algae is eutrophication. A novel method is proposed to discover the association of algae with physicochemical variables related to the algae growth in Erhai Lake, by integrating Maximal information coefficient(MIC) and association rules. To measure the correlation between variables, a new aspect on association rules with non-occurring domain is purposed, which decreases one item in antecedent. The correlation results present significantly in the density levels of algae, which is beneficial to control the level of physicochemical in the water for preventing the increasing number of algae.

Framework for finding maximal association rules in mobile web service environment using soft set

Electronic commerce is very popular nowadays. It is a fast and convenient way to transfer information and communicate with people. E-commerce uses various web services to perform a specific task. When a particular user accessed web services, they are sequentially stored into a database that is called web service sequences. Association rules are used to correlate different web services for knowledge prediction. In this paper, we design a framework for generating maximal association rules of accessed web service sequences using soft set. Soft set uses binary values for their standard representation. This framework converts web service sequences into Boolean-valued information system using the concept of coexistence attributes in a sequence. We define the concept of maximal association rules between attribute sets. Here, maximal support and confidence are also defined using soft set. Experimental results show that the proposed soft-set-based framework provides identical rules when compared with other maximal association rules and rough-set-based rules.

Soft set based association rule mining

Association rules, one of the most useful constructs in data mining, can be exerted to capture interesting dependencies between variables in large datasets. Herawan and Deris initiated the investigation of mining association rules from transactional datasets using soft set theory. Unfortunately, some existing concepts in the literature were unable to realize properly Herawan and Deris’s initial idea. This paper aims to offer further detailed insights into soft set based association rule mining. With regard to regular association rule mining using soft sets, we refine several existing concepts to improve the generality and clarity of former definitions. Regarding maximal association rule mining based on soft sets, we point out the drawbacks of some existing definitions and offer some way to rectify the problem. A number of new notions, such as transactional data soft sets, parameter-taxonomic soft sets, parameter cosets, realizations and M-realizations of parameter sets are proposed to facilitate soft set based association rule mining. Several algorithms are designed to find M-realizations of parameter sets or extract σ-M-strong and γ-M-reliable maximal association rules in parameter-taxonomic soft sets. We also present an example to illustrate potential applications of our method in clinical diagnosis. Moreover, two case studies are conducted to highlight the essentials of soft set based association rule mining approach.

Applying Dependency of Attributes for Business Data Mining in Information Systems: A Theoretical Framework

This paper presents the applications of dependency of attributes in information systems for data mining from business datasets. Firstly, we present the theoretical framework for data clustering on small business dataset. It is based on a construction of a hierarchical rough set approximation in an information system for data splitting. The hierarchy is defined by the notion of a nested sequence of indiscernibility relations that can be defined from the dependency of attributes. Secondly, an application of such hierarchy for mining maximal association from a business transactional data is presented. It is shown that the dependency provides clear and provable theoretical approach for data clustering and maximal association rules mining.

Grade prediction improved by regular and maximal association rules

In this paper we propose a method of predicting student scholar performance using the power of regular and maximal association rules. Due to the large number of generated rules, traditional data mining algorithms can become difficult and inappropriate to educational systems. Thus, we use some methods to overcome this problem, discovering rules useful in educational process. These methods are applied to the e-learning system Moodle, for “Database” course.

PMCR-Miner: parallel maximal confident association rules miner algorithm for microarray data set.

The MCR-Miner algorithm is aimed to mine all maximal high confident association rules form the microarray up/down-expressed genes data set. This paper introduces two new algorithms: IMCR-Miner and PMCR-Miner. The IMCR-Miner algorithm is an extension of the MCR-Miner algorithm with some improvements. These improvements implement a novel way to store the samples of each gene into a list of unsigned integers in order to benefit using the bitwise operations. In addition, the IMCR-Miner algorithm overcomes the drawbacks faced by the MCR-Miner algorithm by setting some restrictions to ignore repeated comparisons. The PMCR-Miner algorithm is a parallel version of the new proposed IMCR-Miner algorithm. The PMCR-Miner algorithm is based on shared-memory systems and task parallelism, where no time is needed in the process of sharing and combining data between processors. The experimental results on real microarray data sets show that the PMCR-Miner algorithm is more efficient and scalable than the counterparts.

MCR-Miner: Maximal Confident Association Rules Miner Algorithm for Up/Down-Expressed Genes

DNA microarrays allow simultaneous measurements of expression levels for a large number of genes across a number of different experimental conditions (samples). The algorithms for mining association rules are used to reveal biologically relevant associations between different genes under different experimental samples. This paper presents a new column-enumeration based method algorithm (abbreviated by MCR-Miner) for mining maximal high confidence association rules for up/down-expresse d genes. MCR-Miner algorithm uses an efficient maximal association rules tree data structure (abbreviated by MAR-Tree). MAR-tree enumerates (lists) all genes with their binary representations, the binary r epresentation of a gene saves the status (normal, up, and down- expressed) of a gene in all experiments. The binary representation has many advantages, scan the dataset only once, the measurements of confidences for association rules are made in one step, and it makes MCR-Miner algorithm easily finds all maximal high confidence association rules. In the experimental results on a real microarray data sets, MCR-Miner algorithm attained very promising results and outperformed other counterparts.

Evaluation and optimization of frequent, closed and maximal association rule based classification

Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand. The algorithms for closed and maximal itemsets mining significantly reduce the volume of rules discovered and complexity associated with the task, but the implications of their use and important differences with respect to the generalization power, precision and recall when used in the classification problem have not been examined. In this paper, we present a systematic evaluation of the association rules discovered from frequent, closed and maximal itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriate sequence of usage. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided as a whole and w.r.t individual classes. Empirical results confirm that with a proper combination of data mining and statistical analysis, a large number of non-significant, redundant and contradictive rules can be eliminated while preserving relatively high precision and recall. More importantly, the results reveal the important characteristics and differences between using frequent, closed and maximal itemsets for the classification task, and the effect of incorporating statistical/heuristic measures for optimizing such rule sets. With closed itemset mining already being a preferred choice for complexity and redundancy reduction during rule generation, this study has further confirmed that overall closed itemset based association rules are also of better quality in terms of classification precision and recall, and precision and recall on individual class examples. On the other hand maximal itemset based association rules, that are a subset of closed itemset based rules, show to be insufficient in this regard, and typically will have worse recall and generalization power. Empirical results also show the downfall of using the confidence measure at the start to generate association rules, as typically done within the association rule framework. Removing rules that occur below a certain confidence threshold, will also remove the knowledge of existence of any contradictions in the data to the relatively higher confidence rules, and thus precision can be increased by disregarding contradictive rules prior to application of confidence constraint.

Improving the Medical Diagnosis Process Using Maximal Association Rules

In this paper, methods based on regular association rules and improved by maximal association rules are developed for discovering interesting associations between medical images and diagnoses. The proposed methods have important characteristics that make them different from other computer assisted diagnosis methods: the process is automatic, having the possibility to define a great number of diagnoses; the methods could be applied to any medical domain, because the visual features, the semantic indicators remain unchangeable, and the semantic rules are generated by learning from labeled images-examples; the selection of the visual characteristics set is based on their retrieval accuracy; the spatial information of the regions is considered, offering important medical information of the relationships between sick regions. Although we present the results achieved in endoscopic images analysis, our methods can be used to analyze other types of medical images.

A soft set approach for association rules mining

In this paper, we present an alternative approach for mining regular association rules and maximal association rules from transactional datasets using soft set theory. This approach is started by a transformation of a transactional dataset into a Boolean-valued information system. Since the “standard” soft set deals with such information system, thus a transactional dataset can be represented as a soft set. Using the concept of parameters co-occurrence in a transaction, we define the notion of regular and maximal association rules between two sets of parameters, also their support, confidence and maximal support, maximal confidences, respectively properly using soft set theory. The results show that the soft regular and soft maximal association rules provide identical rules as compared to the regular and maximal association rules.

Generalized domination in closure systems

In the context of extracting maximal item sets and association rules from a binary data base, the graph-theoretic notion of domination was recently used to characterize the neighborhood of a concept in the corresponding lattice. In this paper, we show that the notion of domination can in fact be extended to any closure operator on a finite universe and be efficiently encoded into propositional Horn functions. This generalization enables us to endow notions and algorithms related to Formal Concept Analysis with Horn minimization and minimal covers of functional dependencies in Relational Databases.

Maximal Association Rules: A Tool for Mining Associations in Text

We describe a new tool for mining association rules, which is of special value in text mining. The new tool, called maximal associations, is geared toward discovering associations that are frequently lost when using regular association rules. Intuitively, a maximal association rule $${X}\stackrel{\rm max}{\Longrightarrow}{Y}$$ says that whenever X is the only item of its type in a transaction, than Y also appears, with some confidence. Maximal associations allow the discovery of associations pertaining to items that most often do not appear alone, but rather together with closely related items, and hence associations relevant only to these items tend to obtain low confidence. We provide a formal description of maximal association rules and efficient algorithms for discovering all such associations. We present the results of applying maximal association rules to two text corpora.

A rough set model with ontologies for discovering maximal association rules in document collections

In this paper we investigate the applicability of a Rough Set model and method to discover maximal associations from a collection of text documents, and compare its applicability with that of the maximal association method. Both methods are based on computing co-occurrences of various sets of keywords, but it has been shown that by using the Rough Set method, rules discovered are similar to maximal association rules, and it is much simpler than the maximal association method. In addition, we also present an alternative strategy to taxonomies required in the above methods, instead of building taxonomies based on labelled document collections themselves. This is to effectively utilise ontologies which will increasingly be deployed on the Internet.