Multilevel Association Rules 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.

Enhancing Customer Relationship Management Using Fuzzy Association Rules and the Evolutionary Genetic Algorithm

The importance of Customer Relationship Management (CRM) has never been higher. Thus, companies are forced to adopt new strategies to focus on customers, given the competitive climate in which they operate. Also, companies have been able to maintain customer data within large databases that contain all information related to customers, thanks to the tremendous technological development seen recently. Multilevel quantitative association mining is a significant field for achieving motivational associations between data components with multiple abstraction levels. This paper develops a methodology to support CRM to improve the relationship between retail companies and their customers in the retail sector to retain existing customers and attract more new customers, by applying data mining techniques using the genetic algorithm through which an integrated search is performed. The proposed model can be implemented because the proposed model does not need the minimum levels of support and trust required by the user, and it has been confirmed that the algorithm proposed in this research can powerfully create non-redundant fuzzy multi-level association rules, according to the results of these experiments.

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.

TOP-DOWN AND BOTTOM-UP APPROACH FOR MINING MULTILEVEL ASSOCIATION RULES FROM CONCEPT HIERARCHICAL DATA IN DISTRIBUTED ENVIRONMENT

Hierarchical Data mining using distributed environment is an imperative in big data analysis. Multilevel association rules can provide more substantial information than single level rules, and it also determines hierarchical knowledge from the dataset. Nowadays, numerous e-commerce and social networking sites generates vast amount of structural/semi-structural data in the form of sales data, tweets, text mails, web usages and so on. The data generated from such sources is so large that it becomes very difficult to process and analyze it using conventional approaches. This paper overcomes the computing limitation of single node by distributing the task on multi-node cluster. The performance of this system is compared based on minimum support threshold at diverse levels of concept hierarchy and by varying the dataset size. In this paper, the transactional dataset is created from huge sales dataset using Hadoop MapReduce framework. Then, two distributed multilevel frequent pattern mining algorithms MR-MLAB (MapReduce based Multilevel Apriori using Bottom-up approach) and MR-MLAT (MapReduce based Multilevel Apriori using Top-down approach) are implemented to find interesting level-crossing frequent itemset for each level of concept hierarchy. The hierarchical redundancy in multilevel association rules affects the quality of the market basket analysis. Hence, to improve the performance of the system, the hierarchical redundancy has to be removed from it. Finally, the time efficiency of proposed algorithms is compared with existing Traditional Multilevel Apriori (TMLA) Algorithm. The proposed algorithms with MapReduce framework are found efficient compared to the traditional algorithms.

Algorithm Study of New Association Rules and Classification Rules in Data Mining

In order to further improve the efficiency of data mining, it proposes a kind of data mining algorithm based on new association rules and classification rules. Specific research process is as follows. Firstly, MDML-PP algorithm is analyzed and applied to the multi-dimensional multi-level association rules in data mining, and then selects the test data sets for performance evaluation, meanwhile, it also studies the multi-support rate cut and classification rule mining algorithm, which is applied to multi-support rate classification rules in data mining, finally, using the compare experiment to verify the effectiveness of multi-support rate classification rules. The results show that the proposed algorithm in this paper can be better used in data mining with multi-dimensional and multi-layer association rules, which can improve the efficiency and quality of data mining.

Abnormal Event Health-Status Monitoring Based on Multi-Dimensional and Multi-Level Association Rules Constraints in Nursing Information System

Since the traditional methods such as statistical analysis are used to analyze the long-term nursing service strategy more, and no association rule method is used to solve the problems encountered in the development of long-term nursing service. This paper aims to provide some data support for the establishment of a scientific and reasonable long-term nursing information system by synthesizing the basic national conditions and drawing lessons from foreign experience. Therefore, in order to improve the analytical accuracy of multidimensional multi-model data, minimal rule which includes rules with single item as the consequent and the minimal number of items as the antecedent, can be introduced to derive the same decisions as other association rules without information loss, while the number of minimal rules is much less than of all rules. From these association rules, it can be known that diabetes has a strong association with coronary heart disease, hypertension, fatty liver, chronic arterial occlusive disease, abnormal lipid metabolism and other diseases; according to the analysis of the antecedent and consequence in the results table of the above association rules, it can be found that diabetes is highly likely to cause hypertension, coronary heart disease, abnormal lipid metabolism, fatty liver, and there is a close relationship between various complications.

A drug association based inventory control system for ambulatory care

The purpose of the research is to explore an innovated drug inventory control system for medical organization to improve the system performance in terms of daily patients’ prescriptions and drugs flows. It is not easy to well control drugs inventory due to large numbers of stock keeping units and huge quantities of them in a medical organization. In order to maintain both the daily jobs of ambulatory care and the rights of the patients, the availability of all drugs keeping at a reasonable stock level for medical staffs and patients is required. Also, to avoid increase of cost due to over stocking to overcome the prediction of possible shortage of drugs is a must. In traditional control system, cost is always be considered as the decision variable. Based on the total cost of each item in an inventory management within a planning cycle, and to calculate out the quantity for that particular item within the total inventory, the total amount of inventory ratio for that item will also be derived and be classified to make different inventory management strategies for different catalogues. There are hundreds of variable pharmaceutical goods in medical organization, hence, large number of prescription data and critical information will be ignored if the inventory classification is determined by the traditional cost approach. This study used association rules analysis to mining the patient treatments database, in order to derive prescription-oriented association rules for improving cost-oriented categories inventory management. In this research, data mining technology is utilized to detect hiddenly issues, which might cause drug shortages. Around 480,000 data, ranges from January to March, 2010, were extracted from Taiwan’s National Health Insurance files, and applied with multi-level association rule analysis, 19 association rules were obtained. In conclusion, the potential relationship in a huge database, found by data mining technique, can be used to support managing staff to adjust inventory policies accordingly.

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.

Fast mining algorithm for multi-level association rule data under temporal constraints

Redundant interference occurs between frames of multi-level association rule data under temporal constraints, which brings poor clustering and anti-interference performance to data mining. In order to improve the multi-level association rule data mining ability, this paper proposes a fast mining algorithm for multi-level association rule data based on temporal constraints. It constructs a fitting state model of multi-level association data distribution, and uses the reorganisation method of multi-level association rules to re-arrange data structure and extract the average mutual information feature; it constructs detection statistics to conduct multi-level linear programming design for association rules data, and uses the autocorrelation detection method to conduct de-interference processing and the fuzzy directional clustering method to conduct fuzzy clustering processing for multi-level association rule data, to realise fast mining of multi-level association rule data under temporal constraints. The simulation results show that compared with traditional methods, the proposed method reduces the execution time of multi-level association rule data mining by 12.77%, and the mining accuracy is improved by 23.34%. High mining accuracy and strong anti-interference ability make the data mining efficiency improved.

Fast mining algorithm for multi-level association rule data under temporal constraints

Redundant interference occurs between frames of multi-level association rule data under temporal constraints, which brings poor clustering and anti-interference performance to data mining. In order to improve the multi-level association rule data mining ability, this paper proposes a fast mining algorithm for multi-level association rule data based on temporal constraints. It constructs a fitting state model of multi-level association data distribution, and uses the reorganisation method of multi-level association rules to re-arrange data structure and extract the average mutual information feature; it constructs detection statistics to conduct multi-level linear programming design for association rules data, and uses the autocorrelation detection method to conduct de-interference processing and the fuzzy directional clustering method to conduct fuzzy clustering processing for multi-level association rule data, to realise fast mining of multi-level association rule data under temporal constraints. The simulation results show that compared with traditional methods, the proposed method reduces the execution time of multi-level association rule data mining by 12.77%, and the mining accuracy is improved by 23.34%. High mining accuracy and strong anti-interference ability make the data mining efficiency improved.

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.

Multilevel Association Rules in Data Mining

Multilevel Association Rules in Data Mining

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.

P-CSREC: A New Approach for Personalized Cloud Service Recommendation

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user’s score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy.

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.

Optimasi Alokasi Produk pada Ruang Rak Displai Gerai Minimarket Berdasarkan Harga Produk Menggunakan Multilevel Association Rules

Optimization of Products Shelf Space Allocation Based on Product Price Using Multilevel Association Rules. Product allocation, product assortment, and product price have a significant influence on customer buying behavior. With limitation on shelf space, retailer must select, pricing, and allocate the products on shelf space optimally to maximize the profit for retailer. This research is focused on optimizing the products shelf space allocation based on the relationship between product categories and product price using data mining technique, multilevel association rules. Takes advantage of data transactions, 9 associations between categories, 24 associations between subcategories, and 67 associations between products were obtained. By using zero one integer programming selected 61 products with appropriate price that must be maintained to be allocated in the minimarket to maximize the retailer’s profit. The result, product allocation configuration based on the relationship between product categories and product price is shown. Keywords: Product Allocation, Data Mining, Multilevel Association Rules, Zero One Integer Program, Pricing

Pengalokasian Produk pada Ruang Rak Displai Gerai Minimarket Menggunakan Multilevel Association Rules

Product allocation and product assortment have a significant influence on customer buying behavior. With limitation on shelf space, retailer must determine the customer buying behavior to maximize the profit for retailer. This research is focused on the products shelf space allocation based on the association between product categories and subcategories using data mining technique, multilevel association rules. Takes advantage of data transactions, 4 associations between categories and 11 associations between subcategories products were obtained. The degrees of activity relationship between categories from 4 associations obtained by Activity Relationship Chart method. The result, product allocation configuration based on the associations between product categories and degree of activity relationship is shown. Keywords: Product Allocation, Data Mining, Multilevel Association Rules, Activity Relationship Chart