Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems

Data mining has been emerging recently as a viable computational tool for autonomous decision making especially in the field of medical applications. It has provided diagnostic solutions for skin and breast cancer detection, brain tumor detection, and also for other classification problems. In this chapter, we explore two data mining techniques, namely, association mining and decision tree mining, for predicting the life span of the kidney failure patients who have undergone routine dialysis. The total parameters used for this study were 28 attributes. The optimal prioritized parameters that decide the survival rate are reported and it can be concluded from the experimental results that the decision tree approach yields promising results.

Cardiovascular disease has increased rapidly in the past few decades. It has become a leading cause of death globally. Heart disease has affected the global heterogeneous population irrespective of age and gender. According to World Health Organization, an estimated 17.3 million people died from cardiovascular diseases in 2008, representing 30% of all global deaths. The accurate and timely prediction of these diseases has become a challenge for medical organizations. A mere assumption of absence or presence of disease is an approach used by many hospitals to give prediction results. The predictions of the heart disease are dependent mainly on the prominent factors involved and their effect weightage. Finding out the patterns and extracting knowledge from those patterns is the major task at hand. Data mining techniques have proven to be a good means for this knowledge discovery. This study makes use of the prominent features of two data mining techniques, namely, K-Means Clustering and Decision Tree. These methods, one being unsupervised learning and the other supervised learning, use very different approaches to predict the results. The positive factors of both the techniques have been used to build a Hybrid Classifier. The aim is to provide an algorithm which gives the best accuracy and performance for the Heart disease identification system.

Pattern mining algorithms for data streams using itemset

Recent advances in information technology have made it possible to solve increasingly complex problems, and also to collect and store huge amounts of information. These vast quantities of data further have to be transformed into relevant value-added and “decision-quality” knowledge. It is against this background that the KDD (Knowledge Discovery in Databases), a multidisciplinary field using computer learning, artificial intelligence, statistics, database technology, expert systems, and data visualization, appeared in the early 90's. In order to assess these technologies in the medical field, we have tested some of these techniques on a large database at our disposal, named DiabCare stemming from the WHO – DiabCare program for the application of the Saint-Vincent Declaration. It contains evaluation data on the health care of patients with diabetes, and in particular, its complications. So far, data analysis has been done using classical statistical methods, and we now intend to make use of such data-mining tools as Associations Rules and Decision and Classification Trees for further exploration of this database. The results presented here show that data mining techniques can be used successfully to extract knowledge from medical databases. The results obtained using Association Rules and especially Decision Trees are very promising.

Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets in datasets and predicts the associative and correlative behaviors for new data. Rooted in market basket analysis, there are a great number of techniques developed for association rule mining. They include frequent pattern discovery, interestingness, complex associations, and multiple data source mining. This paper introduces the up‐to‐date prevailing association rule mining methods and advocates the mining of complete association rules, including both positive and negative association rules. © 2011 John Wiley & Sons, Inc.WIREs Data Mining Knowl Discov2011 1 97‐116 DOI: 10.1002/widm.10This article is categorized under:Algorithmic Development > Association Rules

Using data mining's decision tree classification, DBSCN cluster analysis, and K-nearest neighbor algorithm realizes the information mining of natural gas pipeline leak, and alse uncovers the objective laws behind the natural gas pipeline transmission, intrinsically linking to the each parameter and development trend. We could reduce the risk of accidents and economic losses, in order to control the natural gas transmission in advance.

Controlled clinical trials are studies done by scientists on a concrete domain with the objective of execute a prospective analysis that attempts to compare the effect and value of one or more interventions in humans under specific conditions. Our case study is focused on applying artificial intelligence techniques to support scientists working on bio-nutritional trials, which rely on the study of the effect of consuming food with biomarkers on human health. This work is developed under the HENUFOOD2project. In order to simplify the domain, two trial stages are considered: the trial experimental execution and the trial data analysis. It is after the trial has been executed and all the information collected, when we try to discover non obvious knowledge among the trial data and support the scientists on their everyday analytical tasks. On the one hand, this analysis includes generic statistical manipulation and data discovery empowered by data mining techniques. On the other hand, a scientist support engine has been developed in order to allow non data manipulation and analysis experts to access the information and ask for new analysis processes. The approach, thanks to a user friendly interface enabled with query answering techniques, is able to interact with the user in a conversational mode and run analytical or data discovery processes, do a semantic search on previous indexed works or, finally, infer new rules to feed the expert system ontology. The expert system is not fully covered on this paper, as it will focus on the explanation of the usage of data mining and query answering techniques on that bio-nutritional focused expert system.

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

Human resource is one of the functions of a company that is considered as an asset. Therefo re, the theory of performance qualificat ion was adopted by the company in order to get an overview of employee performance. Furthermore, the company needs an effective method to predict the performance not only for the employees but also for the new applic ants. The goals of this research are to get a decision tree model of the employee performance. By learning employee data, the performance of the new applicants could be predicted. The study would provide the characteristic of new applicants who will give better performance than other applicants . The data from a company in Indonesia will have been used for this research. The data mining technique will be applied to the data of operators (such as admins, clerks, cashiers, machine operators, and security offi cers). The data mining technique was use d is decision tree. The decision tree technique was commonly used for a supervised learning data. The decision tree technique also has advantages compared others, because of its ability to produce information that is easy to understand. The result of this research shown the high dependency of employee performance with employment type (work contract). It also means that employees are encouraged to provide good performance to the company if those employees have become p ermanent employees. This research also showed that there is no relationship between employee performances with gender or position grade.

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

Diabetes is one of the major health issues among youngsters nowadays because of poor diet, family history, lifestyle habits. The medical diagnosis is important based on complicated risks that should be performed accurately and effectively. Based on the result and reports further investigation is needed through diagnosis and treatment are given to the patient. Many numbers of the data can be available in the healthcare systems. The availability and necessary medical data can be analyzed for data analysis tools to extract useful information through pattern recognition. Both data mining and knowledge discovery data are infinite applications including business and research scientific fields. Diabetes is one of the major applications nowadays where data mining tools are very useful to easily diagnose and find the solution. This paper, diagnoses diabetes through data mining tools such as SVM, association rule, clustering, and association. So, Data mining helps to predict and diagnose diseases with a low occurrence of risks. In this paper, the main focus is to make a present detailed survey of various data mining techniques and approaches that have been put to use for the prognosis of diabetes.

Chapter 26 - Relevance of data mining techniques in real life

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Association rules provide an important clue on how hidden knowledge and patterns can be extracted and the attributes extracted are associated when big data is mined by means of a particular data mining algorithm. The context of HEIs, where big data mining is uncommon, there is a need to know how association rules could be utilized to discover hidden knowledge and forecast student performance based on time-to-degree and cumulative grade point average (CGPA) and also make decisions. This research has demonstrated this by taking the example of the dataset of 337 graduated students of a university in Bahrain belonging to the Bachelor's degree in Accounting and Finance. Two types of data mining concepts were applied, regression and a priori algorithm. Regression results showed that there is a statistically substantial link between time-to-degree and course-difficulty and not CGPA. Similarly, association rules showed the link between time-to-degree, course-taking pattern, no. of courses and course-difficulty pattern but not CGPA. The association rules generated provided the basis to predict optimum time-to-degree by controlling the no. of courses, course-taking pattern, and course-taking difficulty. The findings of this research provide an important window of opportunity for HEIs to enhance student performance using data mining techniques. This investigation has contributed theoretically showing the application of data mining theory using apriori algorithm and regression, for HEI big data mining where student factors and organizational factors are said to be fairly different from that of the HEIs where research on big data is seen to be a well-known activity.

Product arrangement, known as product display, is the layout or way of structuring products, especially products companies apply to attract consumers. Although one of the scopes of sales in the market is the delivery of information through galleries, storefronts require specialization and the ability to arrange products or displays following the standards and specifications of the company and the product itself. The resulting output directly affects the level of sales and information to be achieved in a store, especially in modern retail stores with a self-service format (self-service) such as minimarkets. To implement a display system, Data Mining is applied as a determinant of the layout of the merchandise. According to (Kusrini & Lutfi 2009), data mining is used to extract knowledge from databases. One of the many methods in Data Mining is the Apriori Algorithm. The a priori algorithm is a type of association rule in data mining. Association rule or association rule mining is used to find associative rules between item combinations to be used as wisdom to make decisions and provide a stock of products that consumers prefer