Mining Profitability of Telecommunication Customers Using K-Means Clustering

Data mining is the powerful technique, which can be widely used for discovering the customers’ behaviors as well as customer’s preferences. As a result, it has been widely used in top level companies for evaluating their Customer Relationship Management (CRM) system today. In this study, a new K-means clustering method proposed to evaluate the cluster customers’ profitability in telecommunication industry in Sri Lanka. Furthermore, RFM model mainly used as an input variable for K-means clustering and distortion curve used to identify optimal number of initial clusters. Based on the results, telecommunication customers’ profitability in Sri Lanka mainly categorized into three levels.

Campus Economic Analysis Based on K-Means Clustering and Hotspot Mining

With the advent of the era of big data and the development and construction of smart campuses,the campus is gradually moving towards digitalization,networking and informationization.The campus card is an important part of the construction of a smart campus,and the massive data it generates can indirectly reflect the living conditions of students at school.In the face of the campus card,how to quickly and accurately obtain the information required by users from the massive data sets has become an urgent problem that needs to be solved.This paper proposes a data mining algorithm based on K-Means clustering and time series.It analyzes the consumption data of a college student’s card to deeply mine and analyze the daily life consumer behavior habits of students,and to make an accurate judgment on the specific life consumer behavior.The algorithm proposed in this paper provides a practical reference for the construction of smart campuses in universities,and has important theoretical and application values.

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.

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information.Precision agriculture uses datamining to advance agricultural developments.Many farmers aren’t getting the most out of their land because they don’t use precision agriculture.They harvest crops without a well-planned recommendation system.Future crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical data.Most existing research still needs to address data preprocessing and crop categorization/classification.Furthermore,statistical analysis receives less attention,despite producing more accurate and valid results.The study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH values.The research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high productivity.The presented algorithm converts discrete numerals to factors first,then reduces levels.Second,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation.The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and geo-location.Finally,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

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.

Clustering Approach for Analyzing the Student’s Efficiency and Performance Based on Data

The academic community is currently confronting some challenges in terms of analyzing and evaluating the progress of a student’s academic performance. In the real world, classifying the performance of the students is a scientifically challenging task. Recently, some studies apply cluster analysis for evaluating the students’ results and utilize statistical techniques to part their score in regard to student’s performance. This approach, however, is not efficient. In this study, we combine two techniques, namely, k-mean and elbow clustering algorithm to evaluate the student’s performance. Based on this combination, the results of performance will be more accurate in analyzing and evaluating the progress of the student’s performance. In this study, the methodology has been implemented to define the diverse fascinating model taking the student test scores.

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.

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.

A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication

Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets.This paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering technique.This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm.To evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering algorithms.The proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity indices.The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.

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.

Hybrid Data Mining Models for Predicting Customer Churn

The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases;the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.

Distance function selection in several clustering algorithms

Most clustering algorithms need to describe the similarity of objects by a predefined distance function. Three distance functions which are widely used in two traditional clustering algorithms k-means and hierarchical clustering were investigated. Both theoretical analysis and detailed experimental results were given. It is shown that a distance function greatly affects clustering results and can be used to detect the outlier of a cluster by the comparison of such different results and give the shape information of clusters. In practice situation, it is suggested to use different distance function separately, compare the clustering results and pick out the 搒wing points? And such points may leak out more information for data analysts.

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.

基于K-means聚类算法的火电机组两个细则考核分析

在“碳中和”总目标约束下,火电机组的利用小时数越来越低,在推进电力市场化进程中,辅助服务考核对发电厂运行影响甚大,其中,对发电厂自动发电控制(AGC),自动电压控制(AVC)和一次调频的投入率、调节等指标的考核标准进行了严格的规定。AGC调节性能指标不仅影响电网对电厂细则考核费用,而且还影响调频辅助服务相关的指标和市场份额,对电厂的经济性影响很大,治理好AGC调节性能指标,将对电厂提高经济性有直接的影响。针对电网对火电机组的考核信息发布滞后,考核结果不透明,电厂对自身考核原因不明确,缺乏有效分析手段等问题,提出了通过SIS实时在线数据,计算AGC调节性能指标,实时监测AGC调节性能指标,通过Knime数据分析平台利用K-means聚类算法对考核结果进行数据分析,根据影响机组调节特性的特点,对基于影响考核细则调节特性的关键参数工况划分,找到历史上AGC调节性能指标差的运行工况区间,辅助电厂快速定位和分析问题原因。算例选取了江苏某电厂600 MW机组,对该机组1个月的AGC调节性能考核数据进行了分析。在AGC投运率方面,计算结果与电网实际结果相同;在AGC调节精度方面,月均值和每日平均调节精度的计算结果与电网考核结果其误差在可控范畴之内,趋势相同;在实测中可以主要按照工况连续运行数据确定主要聚类参数,并根据省级调度模式,自动计算并储存每条AGC调节精度数据,并清洗和筛选有效数据提供聚类和分析;对聚类后考核结果分析,可得到机组考核快速定位和分析AGC调节性能相关影响因素和产生问题的原因。该方法能够有效挖掘出影响机组AGC调节性能的运行工况区域,帮助电厂调整运行和控制策略,减少电网考核补偿,提高电力市场竞争力。

Study on the Grouping of Patients with Chronic Infectious Diseases Based on Data Mining

Objective: According to RFM model theory of customer relationship management, data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the management of patients with different characteristics. Methods: 170,246 outpatient data was extracted from the hospital management information system (HIS) during January 2016 to July 2016, 43,448 data was formed after the data cleaning. K-Means clustering algorithm was used to classify patients with chronic infectious diseases, and then C5.0 decision tree algorithm was used to predict the situation of patients with chronic infectious diseases. Results: Male patients accounted for 58.7%, patients living in Shanghai accounted for 85.6%. The average age of patients is 45.88 years old, the high incidence age is 25 to 65 years old. Patients was gathered into three categories: 1) Clusters 1—Important patients (4786 people, 11.72%, R = 2.89, F = 11.72, M = 84,302.95);2) Clustering 2—Major patients (23,103, 53.2%, R = 5.22, F = 3.45, M = 9146.39);3) Cluster 3—Potential patients (15,559 people, 35.8%, R = 19.77, F = 1.55, M = 1739.09). C5.0 decision tree algorithm was used to predict the treatment situation of patients with chronic infectious diseases, the final treatment time (weeks) is an important predictor, the accuracy rate is 99.94% verified by the confusion model. Conclusion: Medical institutions should strengthen the adherence education for patients with chronic infectious diseases, establish the chronic infectious diseases and customer relationship management database, take the initiative to help them improve treatment adherence. Chinese governments at all levels should speed up the construction of hospital information, establish the chronic infectious disease database, strengthen the blocking of mother-to-child transmission, to effectively curb chronic infectious diseases, reduce disease burden and mortality.

Parallel K-Means Algorithm for Shared Memory Multiprocessors

Clustering is the task of assigning a set of instances into groups in such a way that is dissimilarity of instances within each group is minimized. Clustering is widely used in several areas such as data mining, pattern recognition, machine learning, image processing, computer vision and etc. K-means is a popular clustering algorithm which partitions instances into a fixed number clusters in an iterative fashion. Although k-means is considered to be a poor clustering algorithm in terms of result quality, due to its simplicity, speed on practical applications, and iterative nature it is selected as one of the top 10 algorithms in data mining [1]. Parallelization of k-means is also studied during the last 2 decades. Most of these work concentrate on shared-nothing architectures. With the advent of current technological advances on GPU technology, implementation of the k-means algorithm on shared memory architectures recently start to attract some attention. However, to the best of our knowledge, no in-depth analysis on the performance of k-means on shared memory multiprocessors is done in the literature. In this work, our aim is to fill this gap by providing theoretical analysis on the performance of k-means algorithm and presenting extensive tests on a shared memory architecture.

Clustering： from Clusters to Knowledge

Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.

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.

基于古籍挖掘的糖尿病中医食疗方调制规律研究

目的 探究糖尿病中医食疗方的调制规律,为糖尿病的临床辨证施食和食疗研究提供参考。方法 收集《中医方剂大辞典》《中医食疗方全录》《中国药膳大辞典》三本纸版古籍中收载的糖尿病食疗方,提取方名、组成、剂型等信息,应用SPSS 26.0进行频数分析及聚类分析,采用SPSS Modeler 18.0开展食物配伍关联分析。结果 共纳入食疗方264首,涉及191种食物。古代糖尿病中医食疗方多用补虚类(47.91%)食药物质及保健食品药材,清热类(11.84%)次之;多以平性(36.65%)、温性(31.41%)为主;味多甘味(55.38%);归经则以脾经(18.99%)、肾经(17.51%)、胃经(16.46%)、肺经(15.40%)居多;汤剂为常见剂型(32.58%);五谷类食物使用较多(25.25%);其主治证型以气阴亏虚(32.07%)为主。结论 古代医家重视由调治中焦入手,灵活应用中医食疗方调治糖尿病,讲究补虚辅以清热利湿,善于理气和中祛湿化浊,妙用“温”品,遵循辨证原则,选材多味,组方多样,为临床进行中医食疗干预糖尿病提供了有益借鉴与思路启发。