Enhanced Association Rules Research Articles

Enhanced Association Rule Mining and Discovering Frequent Patterns in Genomes: Integrated Clustering and Weight Judgement

Enhanced Association Rule Mining and Discovering Frequent Patterns in Genomes: Integrated Clustering and Weight Judgement

A novel algorithm weighting different importance of classes in enhanced association rules

Importance of methods for explainable AI has been ever growing within many processes; a simple form of knowledge is needed by humans who operate the processes. One of the methods suitable for explainable AI is represented by enhanced association rules on categorical data. This method admits an immediate interpretation. A problem arises when we are interested in finding circumstances for a rare category. Even when we find such a category (e.g., in a subset where this category appears five times more), the overall share of this category may be too small for using this rule within processes (e.g., sending someone to an expensive examination, performing calls to clients), and may therefore be refused by process executors (doctors, salesmen). That is why we have created a new algorithm based on association rule mining called UIC-Miner (UIC stands for Unequal Importance of Categories), which overcomes this problem. This algorithm has a pilot implementation for the purposes of testing and reproducing. The algorithm itself uses categories weighted by their levels of importance. This paper first describes a typical situation when category weights are useful. Then, the new algorithm is described, and several examples of its applications are presented. Finally, possible applications of the available tools are discussed.

A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application

A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application

A Dissolved Gas Assessment Model for Power Transformers According to Weighted Association Rule Mining

As one indispensable part of power systems, the reliable-operated power transformers are vital for energy transmission, whereas they are remarkably threatened by potential fault events. To achieve the satisfying and valid operation of power transformers, any fault events that may impact their health ought to be evaluated and early warned. With such motivations, this paper presents original insights on the assessment of power transmission health states via their internal dissolved gas, and an enhanced Association Rule Mining (ARM) model incorporating the analysis of High-Impact-Low-Probability (HILP) components, as well as a dynamic fault event risk evaluation approach, is proposed. The first step is to differentiate the risky components. Unlike the standard ARM, the rarely occurred components in each feature can also be assessed explicitly as the common components to explore the underlying HILP components in the proposed model, rather than just being viewed as trivial data and directly omitted. The second step is to rate the risk level of each risky component. A component importance measure-based evaluation approach is deployed to assess the corresponding risk weights of distinguished risky components. In this approach, the risk weight is determined straightforwardly via the impacts of each component on the variation level of total risks in the system, rather than simply by its frequency of occurrence or data share. Finally, the parameters of the risk weight evaluation approach can be dynamically adapted in an adjustment framework as well. This model is testified through an empirical case study, and the leading results can demonstrate its flexibility and robustness during real applications.

An Enhanced Association Rule Mining Method for Processing Network Comments

An Enhanced Association Rule Mining Method for Processing Network Comments

Association Rules Mining and Statistic Test over Multiple Datasets on TCM Drug Pairs

Objective: TCM drug pair is consisted of two and only two drugs, which is the smallest drug group following special drug compatibility regulations. Formulae compatibility regulations are one of the most important problems in TCM clinical practice and modern research but still not quite resolved. TCM drug pair was a very suitable objects to discovery the complicated formulae compatibility regulations. This paper applied association rules mining to study the structural characters of TCM drug pairs find some special relationships between drugs. This study might give some help to the research on the formulae compatibility regulations. Methods: We presented an enhanced association rules mining method to find out the property associations between two drugs in TCM drug pairs. And a binominal statistic test was introduced to get the statistical significance of rules mined. The property data from the 625 drug pairs containing 347 drugs were collected and analyzed. As most association rules mining run only in single database, the new method was proposed to find rules over multiple databases (2 in this paper standing for the two drugs in TCM drug pairs) based on a first Apriori algorithm mining. Then statistic test was applied to filter out insignificant rules furthermore. Results: Apriori algorithm and the new method were applied to mine association rules on TCM drug pairs for comparison. The rules found by Apriori method showed false high support, part of which came from the property associations within one drug but not between the two drugs in TCM drug pairs. And Apriori method could not found the association of replicated property, such as liver - liver rules. The new method proposed could get the only associations between drugs even those replicated property rules. Some associations were mined with high supports and significances. Conclusion: This paper proposed an enhanced method to perform association rules mining over multiple databases. After comparison with Apriori algorithm the new method could just obtain the associations in which each item came from different database. The method was confirmed to be quite suitable on mining over multiple databases. The statistic test was also necessary to exclude false association rules.

Mining Interesting Least Association Rules in Manufacturing Industry: A Case Study in MODENAS

Least association rules are related to the rarity or uncommonness relationship among itemset in database repository. However, mining these rules are quite tricky and seldom discussed since it usually includes with infrequent items or exceptional cases. In manufacturing industry, detecting these rules is very useful and exciting for further analysis such as for market segmentation, prediction, and product arrangements. In this paper, we introduce an enhanced association rules mining method, called Significant Least Pattern Growth (SLP-Growth) and a new measurement named Critical Relative Support (CRS). The novelty of the proposed method is that unlike existing methods, it is used for capturing interesting least items from line-off database. The data which comprised of production of motorcycle/scooter is taken from Malaysia national motorcycle manufacturer called Motorsikal dan Enjin Nasional Sdn Bhd (MODENAS). The results from this research provide useful information for the management to understand the customer demand trends comprehensibly, and enable them to design marketing strategy accordingly.

Mining critical least association rules from students suffering study anxiety datasets

In data mining, association rules mining is one of the common and popular techniques used in various domain applications. The purpose of this study is to apply an enhanced association rules mining method, so called significant least pattern growth for capturing interesting rules from students suffering from exam, family, presentation and library anxiety datasets. The datasets were taken from a survey among engineering students in Universiti Malaysia Pahang. The results of this research will provide useful information for educators to make more accurate decisions concerning their students, and to adapt their teaching strategies accordingly. Moreover, it can also highlight the role of non-academic staff in supporting learning environments for students. The obtained findings can be very helpful in assisting students to handle their fear and anxiety, and, finally, increasing the quality of the learning processes at the university.

A New Data Stream Mining Algorithm for Interestingness-Rich Association Rules

Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algorithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the association rules. This accelerates the algorithms to mine the association rules based on utility i.e. proficiency of the mined rules. However, fewer algorithms exist in the literature to deal with the utility as most of them deals with reducing the complexity in frequent itemset/association rules mining algorithm. Also, those few algorithms consider only the overall utility of the association rules and not the consistency of the rules throughout a defined number of periods. To solve this issue, in this paper, an enhanced association rule mining algorithm is proposed. The algorithm introduces new weightage validation in the conventional association rule mining algorithms to validate the utility and its consistency in the mined association rules. The utility is validated by the integrated calculation of the cost/price efficiency of the itemsets and its frequency. The consistency validation is performed at every defined number of windows using the probability distribution function, assuming that the weights are normally distributed. Hence, validated and the obtained rules are frequent and utility efficient and their interestingness are distributed throughout the entire time period. The algorithm is implemented and the resultant rules are compared against the rules that can be obtained from conventional mining algorithms.

Mining critical least association rules of student suffering language and social anxieties

One of the commonly and popular techniques used in data mining application is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, so called significant least pattern growth (SLP-growth) proposed by Abdullah et al. (2010a) for capturing interesting rules in student suffering language and social anxieties dataset. The datasets were taken from a survey among engineering students in Universiti Malaysia Pahang (UMP). The results of this research will provide useful information for educators to make a decision on their students more accurately, and to adapt their teaching strategies accordingly. It can be helpful to assist students in handling their fear of language and social. Furthermore, it is also useful in increasing the quality of learning.

Mining Interesting Association Rules of Students Suffering Study Anxieties Using SLP-Growth Algorithm

One of the most popular techniques used in data mining applications is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, called SLP-Growth (Significant Least Pattern Growth) for capturing interesting rules from students suffering mathematics and examination anxieties datasets. The datasets were taken from a survey exploring study anxieties among engineering students in Universiti Malaysia Pahang (UMP). The results of this research provide useful information for educators to make decisions on their students more accurately and adapt their teaching strategies accordingly. It also can assist students in handling their fear of mathematics and examination and increase the quality of learning.

Hardware-Enhanced Association Rule Mining with Hashing and Pipelining

Generally speaking, to implement Apriori-based association rule mining in hardware, one has to load candidate itemsets and a database into the hardware. Since the capacity of the hardware architecture is fixed, if the number of candidate itemsets or the number of items in the database is larger than the hardware capacity, the items are loaded into the hardware separately. The time complexity of those steps that need to load candidate itemsets or database items into the hardware is in proportion to the number of candidate itemsets multiplied by the number of items in the database. Too many candidate itemsets and a large database would create a performance bottleneck. In this paper, we propose a HAsh-based and Pipelined (abbreviated as HAPPI) architecture for hardware- enhanced association rule mining. We apply the pipeline methodology in the HAPPI architecture to compare itemsets with the database and collect useful information for reducing the number of candidate itemsets and items in the database simultaneously. When the database is fed into the hardware, candidate itemsets are compared with the items in the database to find frequent itemsets. At the same time, trimming information is collected from each transaction. In addition, itemsets are generated from transactions and hashed into a hash table. The useful trimming information and the hash table enable us to reduce the number of items in the database and the number of candidate itemsets. Therefore, we can effectively reduce the frequency of loading the database into the hardware. As such, HAPPI solves the bottleneck problem in a priori-based hardware schemes. We also derive some properties to investigate the performance of this hardware implementation. As shown by the experiment results, HAPPI significantly outperforms the previous hardware approach and the software algorithm in terms of execution time.