Multi-level Association Rule Mining Research Articles

Using text mining and multilevel association rules to process and analyze incident reports in China

Incident investigation reports provide information on defects related to the system safety and indications for improvements. Currently, the analysis of these reports relies heavily on expert’ experience. The foreseeable work-load and lack of understanding about the importance of near misses have created a situation where severe accidents are rigorously investigated, and minor incidents are often omitted. Consequently, incident reports have not been fully analyzed to provide sufficient solutions.The aim of this research is to propose a framework that uses text mining and multilevel association rules to efficiently structure Chinese incident reports and identify important incident patterns, providing an analysis of trends, rectification strategies, and guidance for safety management.A case study of a construction company in China was conducted using two years of incident data dated 2018–2019, including accidents and near misses. To identify incident elements, a pattern extraction workflow involving TextRank, and domain pertinence was devised based on the linguistic and writing styles of Chinese reports. A concept hierarchy was applied to determine the taxonomic relationships within the risk factors. Multilevel association rule mining was adopted and proven to deliver more comprehensive pattern indications. Comparative and cross-analysis of patterns in different time periods revealed the severity and temporal features of incidents as well as the effectiveness of preventive and precautionary measures. The results also highlight the importance of learning from near miss events. Decision makers can formulate countermeasures and management policies based on these results to improve safety performance.

Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns

Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production.

Strategic rearrangement of retail shelf space allocations: Using data insights to encourage impulse buying

Brick-and-mortar retailers are struggling to compete with online retailers, who increasingly use customer-generated data to provide highly customized experiences for their patrons to drive impulse purchases. Although brick-and-mortar retailers cannot offer such customization, they must identify innovative ways to transform their own point-of-sale transaction data into increased customer impulse buying. We propose a shelf allocation-relocation scheme for rearranging storewide product allocations over time to maximize impulse buying behavior. The proposed method provides a roadmap for optimally allocating shelf space when routine rearrangements of a retail store take place per store policy. To the best of our knowledge, this represents one of the first periodic storewide rearrangement studies to use customer tendencies with the current shelf arrangement as the basis for the subsequent one. We also introduce a novel approach for incorporating past transaction data insights of cross-selling elasticity into the Shelf Space Allocation Problem (SSAP) to encourage long-term new business creation through impulse purchases. The method applies insights from association rule mining to group highly affine and profitable product pairs, optimize the assignment of departments to store aisles, and determine the optimal within-aisle space allocations for the products of each department. Our major finding is that, based on the potential of generating multiple forms of impulse purchasing (visibility-driven purchasing, present-shelf impulse, and past-aisle impulse), our strategic rearrangement technique consistently outperforms visually rearranging shelf space allocations and several existing allocation techniques that utilize multi-level association rule mining, while, in many instances, exceeding the profit potential of a more traditional unchanged (one-time optimal) shelf space arrangement, depending on the nature of a retailer’s target market. Our results also highlight the importance of a retailer selecting the most appropriate shelf space rearrangement strategy that fits the characteristics of its customers, especially their discretionary income level and their familiarity with the store layout.

Inventory Classification Using Multi-Level Association Rule Mining

Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.

Performance of multi-level association rule mining for the relationship between causal factor patterns and flash flood magnitudes in a humid area

Integrated with K-means clustering and Apriori algorithm, the multi-level association rule mining is proposed to investigate the causal factor patterns of flash floods, which consists of the following three steps: first, the association between causal factors and flash flood occurrence is being analysed; second, to identify the contribution of soil moisture (SM) to flash flood hazards, the association between risk indicators and SM, and the linkage between SM and risk magnitude are being discussed; finally, with the consideration of total 24-h rainfall and SM pattern, the association rules for risk magnitude are extracted. The method has been tested in a humid area of southern China, results show: (1) flash flood hazards are especially active after the prolonged and periodic intense rainfalls, and because of the saturated SM, flash floods are easily triggered even by slight rainfall; (2) severe flash floods are easily triggered by extreme rainfall, and SM is the critical indicator of 5-year floods and 20-year floods; and (3) owing to the differences in steady infiltration rate and instability in soil type, conservation of water and soil is an indispensable and co-ordinate part of flood control. Results are expected to be applicable for decision-making in flood control and flood prediction.

Ordering Policy Using Multi-Level Association Rule Mining

In this article, an inventory model for a retailer's ordering policy is studied. Multi-level association rule mining is used to find frequent item-sets at each level by applying different threshold at different levels. During order quantity estimation, category, content, and brand of the items are considered, which leads to the discovery of more specific and concrete knowledge of the required order quantity. At each level, optimum order quantity of frequent items is determined. This assists inventory manager to order optimal quantity of items as per the actual requirement of the item with respect to their category, content and brand. An example is devised to explain the new approach. Further, to understand the effect of above approach in the real scenario, experiments are conducted on the exiting dataset.

A literature review: big data and association rule mining

In Today’s modern and advanced era, huge amounts of data have become available on hand to developers and choice makers. Big data successfully handles datasets that are not only large, but also very high in velocity and variety, which difficult to handle using conventional techniques, methods and tools. Multilevel association rule mining plays a very vital role in distributed environment in analysis of big data for preparing different Marketing strategies. As compared to Single Level rule, more precise and prominent information is provided by multilevel association rule and additionally from the hierarchical dataset it generates the conceptual hierarchy of knowledge. This paper aims to analyze Data Mining Technique named Multilevel Association rule, which provides additional important information in comparison to single level rule, and it also invents conceptual hierarchy of information/data from the hierarchical dataset. Tools and techniques of Big Data have also been reviewed in detail.

MapReduce Based Multilevel Consistent and Inconsistent Association Rule Detection from Big Data Using Interestingness Measures

Abstract Multilevel association rule mining in distributed environment plays an important role in big data analysis for making marketing strategy. Multilevel association rule provides more significant information than single level rule, and also discovers the conceptual hierarchy of knowledge from the hierarchical dataset. In this era of internet, various online marketing sites and social networking sites are generating enormous amount of data so that it becomes very difficult to process and analyze it using conventional approaches as it consumes more time. This paper overcomes the computing limitation of single node by distributing the task on multi-node cluster. The proposed method initially extracts multilevel association rules including level-crossing for each zone using distributed multilevel frequent pattern mining algorithm (DMFPM). These generated multilevel association rules are so large that it becomes complex to analyze it. Thus, MapReduce based multilevel consistent and inconsistent rule detection (MR-MCIRD) algorithm is proposed to detect the consistent and inconsistent multilevel rules from big hierarchical data which provide useful and actionable knowledge to the domain experts. These pruned interesting rules also give useful knowledge for better marketing strategy. The extracted multilevel consistent and inconsistent rules are evaluated and compared based on different interestingness measures presented together with experimental results that lead to the final conclusions.

A Novel Approach in Mining Specialized Coherent Rules in a Level-Crossing Hierarchy

Multi-level association rule mining techniques that are developed for mining data at multiple-level taxonomy usually rely on minimum support and minimum confidence threshold for extracting frequent items at various levels. But the usage of above threshold alone does not produce meaningful and interesting rules. Moreover, the determination of minimum support and confidence is very hard in a dataset containing multiple-level taxonomy. In order to overcome the above drawbacks, coherent rule mining was proposed. The proposed approach first mines multiple-level coherent rules in a predefined taxonomy and then extracts specialized coherent rules. The rules are mentioned as specialized rules because they exist only in the bottommost level of taxonomy irrespective of their predecessor rules. The proposed algorithm works for the quantitative dataset, and a triangular membership function is used for converting the quantitative dataset at various levels into fuzzy itemset. The results obtained are compared with multi-level fuzzy association rule mining technique.

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the user-specified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.

Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review

Today multilevel association rule mining is an emerging field in data mining. Its main goal is to find hidden information in or between levels of abstraction. It is mainly used for decision making for large data. It focuses on the customer relationship management. Apriori algorithm is mainly used for the multilevel association rule mining. Producing large number of candidate item sets and multiple scanning databases is main shortage of the Apriori algorithm. Because of the multilevel association execution time is reduced and throughput increase in new methods.MRA algorithm using Bayesian probability, concept hierarchy ,COFI-tree method, dynamic concept hierarchy are used for increase performance of multilevel association rule mining.

GA‐based multi-level association rule mining approach for defect analysis in the construction industry

Abstract In construction industry, work defects yield time and cost overruns of construction projects and also cause disputes between project participants during construction and operation phases. To date, there hasn't yet been an adequate analytical model to extract useful information from the database of construction defects. The information represented in the form of association rules could enhance quality management via defect prediction and causation analysis. This paper proposes a Genetic Algorithm (GA)-based approach that incorporates the concept hierarchy of construction defects to discover multi-level patterns of defects from the database of defects in the Chinese construction industry during 2000 to 2010. First, the domain knowledge of construction defect is incorporated into a concept hierarchy to adjust mining items at different levels according to the data sparseness and the interestingness of a rule. Second, a GA-based approach is proposed to generate interesting association rules without specified threshold of minimum confidence, taking advantage of the searching capability of GA. Finally, the redundant rules in the mining results are pruned by post-processing method. A test case is selected to demonstrate the feasibility and applicability of the proposed approach within the problem domain. It is concluded that the proposed method provided an effective tool to discover useful knowledge hidden in historical defect cases. The discovered knowledge indicating relationships between defects and defect causes enables project managers to make strategies for estimating and reducing defects.

Comparison of New Multilevel Association Rule Algorithm with MAFIA

Multilevel association rules provide the more precise and specific information. Apriori algorithm is an established algorithm for finding association rules. Fast Apriori implementation is modified to develop new algorithm for finding frequent item sets and mining multilevel association rules. MAFIA is another established algorithm for finding frequent item sets. In this paper, the performance of this new algorithm is analyzed and compared with MAFIA algorithm.

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining

A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining

Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

Spatially enabled emergency event analysis using a multi-level association rule mining method

Spatial attributes are important factors affecting the entire process of emergency management. Despite the importance of spatial attributes in emergency rescue, this subject has not been subjected to thorough examination in previous literature. This paper presents a new method that incorporates spatial predicates describing the spatial relationships between emergency locations and surrounding objects into emergency event analysis. More specifically, we developed three mechanisms to achieve spatially enabled emergency analysis. First, a novel fact constellation schemas that integrate the spatial predicates and non-spatial data of emergency events is created for multi-level analysis. Second, a filtering algorithm is designed to remove redundant predicates from the huge amount of spatial predicates for data association rule mining. Third, by incorporating the filtering method, a multi-level spatial data association algorithm was proposed to realize the analytical function for emergency events. Experiments were conducted, and the results validate the effectiveness of our method.

Cross-Ontology Multi-level Association Rule Mining in the Gene Ontology

The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into account the structure and semantics of the GO, generates generalized transactions from annotation data and mines interesting multi-level cross-ontology association rules. We applied our method on publicly available chicken and mouse GO annotation datasets and mined 5368 and 3959 multi-level cross ontology rules from the two datasets respectively. We show that our approach discovers more and higher quality association rules from the GO as evaluated by biologists in comparison to previously published methods. Biologically interesting rules discovered by our method reveal unknown and surprising knowledge about co-occurring GO terms.

Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well.

Implementation of Hierarchical Database Attributes Covering in Multidetector A-R Mining Algorithm

With the development of artificial intelligence and data warehouse application development,Intelligent and efficient data mining technology has become the huge data bottleneck, This paper studies stratification theory improved technology to realize the property overrides hierarchical database, Data pretreatment based on the mining algorithm, Through in-depth analysis and research, Improved the A-R algorithm, Realize the problem scope expanded and improved the classical association rules mining algorithm efficiency, Based on the realization of the multilevel association rules mining based on the attribute weights of attributes covering hierarchical database mining methods,To improve the mining knowledge representation systems automation capabilities and data mining algorithm and its application to extended practical problems