Mining association rule efficiently based on data warehouse

The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

Data mining-based analysis of acupoint selection patterns for chronic hepatitis B infection

Background:The purpose of this study was to identify the characteristics and principles of acupoints applied for treating chronic hepatitis B infection.Methods:The published clinical studies on acupuncture for the treatment of chronic hepatitis B infection were gathered from various databases,including SinoMed,Chongqing Vip,China National Knowledge Infrastructure,Wanfang,the Cochrane Library,PubMed,Web of Science and Embase.Excel 2019 was utilized to establish a database of acupuncture prescriptions and conduct statistics on the frequency,meridian application,distribution and specific points,as well as SPSS Modeler 18.0 and SPSS Statistics 26.0 to conduct association rule analysis and cluster analysis to investigate the characteristics and patterns of acupoint selection.Results:A total of 42 studies containing 47 acupoints were included,with a total frequency of 286 acupoints.The top five acupoints used were Zusanli(ST36),Ganshu(BL18),Yanglingquan(GB34),Sanyinjiao(SP6)and Taichong(LR3),and the most commonly used meridians was the Bladder Meridian of Foot-Taiyang.The majority of acupuncture points are located in the lower limbs,back,and lumbar regions,with a significant percentage of them being Five-Shu acupoints.The strongest acupoint combination identified was Zusanli(ST36)–Ganshu(BL18),in addition to which 13 association rules and 4 valid clusters were obtained.Conclusion:Zusanli(ST36)–Ganshu(BL18)could be considered a relatively reasonable prescription for treating chronic hepatitis B infection in clinical practice.However,further high-quality studies are needed.

Study on the medication rules of traditional Chinese medicine in the treatment of sleep disorder after stroke based on data mining

Objective:To explore the medication rule of Traditional Chinese Medicine(TCM)in the treatment of sleep disorder after stroke by using data mining technology.Methods:A computer search was used to search the electronic database of clinical literature on the treatment of sleep disorders after stroke by TCM from January 2000 to January 2021.Excel was used to establish the database,and the prescription information was described and analyzed statistically.Using IBM SPSS Modeler 18.0 software,Apriori algorithm was used for TCM association analysis,and IBM SPSS 22.0 software was used for systematic cluster analysis of high-frequency TCM.Results:A total of 67 literatures were included,covering 131 traditional Chinese medicines.The medecines with a higher frequency of sodium use include Ziziphi Spinosae Semen(Suanzaoren),Angelicae Sinensis Radix(Danggui),Ligusticum(Chuanxiong),liquorice(Gancao),Poria cocos(Fuling),and so on.From the effect point of view,deficiency-tonifying medicine,sedative medicine and blood-activating and stasis-removing medicine are commonly used.The medicinal properties are mainly cold,mild and warm.The main medicine flavor are sweet and bitter.The medicines mostly belong to the liver,heart and spleen Meridian.Thirty-three association rules were obtained for medicine pairs and medicine groups from the correlation analysis,and the core combinations were"Ziziphi Spinosae Semen(Suanzaoren)-Tuber fleeceflower stem(Yejiaoteng)","Ziziphi Spinosae Semen(Suanzaoren)-Polygala(Yuanzhi)","Ziziphi Spinosae Semen(Suanzaoren)-Cortex albiziae(Hehuanpi)"and"Angelicae Sinensis Radix(Danggui)-Radix bupleuri(Chaihu)-Radix Paeoniae Alba(Baishao)"and so on.Seven medicine aggregation groups were obtained by medicine cluster analysis.Conclusion:In the treatment of sleep disorder after stroke by TCM,the main method is to calm the heart and mind.Meanwhile,according to different syndrome types,the treatment methods of tonifying the heart and spleen,nourishing the liver and kidney,soothing the liver and softening the liver,clearing heat and resolving phlegm,nourishing the blood and promoting blood circulation are selected,which provide certain reference for clinical treatment.

抢先赢得商机的Data Mining──基于数据仓库的数据挖掘技术

首先介绍了数据仓库以及在此技术上产生的数据挖掘技术,其次阐述了实现数据挖掘应用的几种工具以及选用工具时应遵循的原则,最后说明了数据挖掘技术现存的问题及他现在重要的商业地位。

A study on the rule of Chinese medicine use for airway remodeling based on data mining

Objective:Use data mining techniques to explore the rule of Chinese medicine used for airway remodeling.Methods:Search the literature on Chinese medicine use for airway remodeling in the past 20 years.With the help of WPS Office Excel 11.1,IBM SPSS Statistics 23.0 and SPSS Modeler 18.0 software,prescriptions were analyzed for the frequency of drug use,the four natures,the five flavours and the channel tropism,cluster analysis and association analysis of high-frequency drugs.Results:There were 58 Chinese medicine prescriptions for airway remodeling be found,involving 105 Chinese medicines,the most frequent channel tropism were spleen,stomach,lung,large intestine,liver and gallbladder,the most frequent use of the five flavors was sour,sweet and pungent,the highest frequency of the four natures was cold and hot,cluster analysis yielded eight drug aggregation groups,and association rule analysis yielded five groups of high-frequency drug pairs.Conclusion:The main TCM treatments for airway remodeling are expelling phlegm,relieving cough,asthma calming,expelling blood stasis and deficiency tonifying.The results of this study can provide ideas for compounding and drug selection for subsequent studies.

An Analysis of Medication Rules of Chinese Medicine for Restless Legs Syndrome Based on Data Mining

Objective: Based on data mining, to explore the medication rules of Chinese medicine for the treatment of restless legs syndrome(RLS). Methods: The CNKI, WANFANG, and VIP were taken as data sources, and "restless legs syndrome, RLS" as the key words, and "Chinese medicine, Chinese materia medica, traditional Chinese medicine(TCM), traditional Chinese and Western medicine" as sub key words, the data was extracted from the journals and literature related to the treatment of RLS by TCM from the establishment of the database to 2020, and data mining techniques(frequency analysis, cluster analysis, association rules) were used to analyze the core drugs and drug pair(group) rules. Results: A total of 87 prescriptions met the requirements of this study, involving 142 Chinese herbal medicines. The top 5 Chinese herbal medicines with a higher frequency of use were Radix Paeoniae Alba, Radix Glycyrrhizae, Radix Angelicae Sinensis, Fructus Chaenomelis and Radix Astragali seu Hedysari. The four Qi(气) of the medicine were mainly warm and neutral, the five flavors were mainly sweet, bitter, and pungent. The main channels of the meridian were mainly the liver meridian, spleen meridian and heart meridian. The medication categories were mainly tonifying deficiency herbs, blood activating and removing blood stasis herbs, and eliminating wind and dampness herbs. The association rule analysis yielded 24 Chinese medicine combinations with high support, and the hierarchical cluster analysis yielded a total of 5 clusters. Conclusion: TCM treatment of RLS is based on tonifying deficiency herbs, especially to replenish Qi and blood throughout the course of the disease, supplemented by herbs for promoting blood circulation and removing blood stasis, and herbs for eliminating wind and dampness, as well as combined with herbs for reliving superficies and herbs for calming the liver to stop the wind.

Data Mining-Based Maintenance Management Framework of Multi-component System

Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

A Fast Distributed Algorithm for Association Rule Mining Based on Binary Coding Mapping Relation

Association rule mining is an important issue in data mining. The paper proposed an binary system based method to generate candidate frequent itemsets and corresponding supporting counts efficiently, which needs only some operations such as ＂and＂, ＂or＂ and ＂xor＂. Applying this idea in the existed distributed association rule mining al gorithm FDM, the improved algorithm BFDM is proposed. The theoretical analysis and experiment testify that BFDM is effective and efficient.

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.

A New Hybrid Algorithm for Association Rule Mining

HA(hashing array),a new algorithm,for mining frequent itemsets of large database is proposed.It employs a structure hash array,ItemArray() to store the information of database and then uses it instead of database in later iteration.By this improvement,only twice scanning of the whole database is necessary,thereby the computational cost can be reduced significantly.To overcome the performance bottleneck of frequent 2-itemsets mining,a modified algorithm of HA,DHA(direct-addressing hashing and array) is proposed,which combines HA with direct-addressing hashing technique.The new hybrid algorithm,DHA,not only overcomes the performance bottleneck but also inherits the advantages of HA.Extensive simulations are conducted in this paper to evaluate the performance of the proposed new algorithm,and the results prove the new algorithm is more efficient and reasonable.

Innovative data mining approaches for outcome prediction of trauma patients

Trauma is the most common cause of death to young people and many of these deaths are preventable [1]. The prediction of trauma patients outcome was a difficult problem to investigate till present times. In this study, prediction models are built and their capabilities to accurately predict the mortality are assessed. The analysis includes a comparison of data mining techniques using classification, clustering and association algorithms. Data were collected by Hellenic Trauma and Emergency Surgery Society from 30 Greek hospitals. Dataset contains records of 8544 patients suffering from severe injuries collected from the year 2005 to 2006. Factors include patients' demographic elements and several other variables registered from the time and place of accident until the hospital treatment and final outcome. Using this analysis the obtained results are compared in terms of sensitivity, specificity, positive predictive value and negative predictive value and the ROC curve depicts these methods performance.

The Application of Book Intelligent Recommendation Based on the Association Rule Mining of Clementine

The traditional library can’t provide the service of personalized recommendation for users. This paper used Clementine to solve this problem. Firstly, model of K-means clustering analyze the initial data to delete the redundant data. It can avoid scanning the database repeatedly and producing a large number of false rules. Secondly, the paper used clustering results to perform association rule mining. It can obtain valuable information and achieve the service of intelligent recommendation.

Algorithm of Intrusion Detection Based on Data Mining and Its Implementation

Intrusion detection is regarded as classification in data mining field. However instead of directly mining the classification rules, class association rules, which are then used to construct a classifier, are mined from audit logs. Some attributes in audit logs are important for detecting intrusion but their values are distributed skewedly. A relative support concept is proposed to deal with such situation. To mine class association rules effectively, an algorithms based on FP-tree is exploited. Experiment result proves that this method has better performance.