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This paper describes a method for improved access to publisher references in linked data RDF editors using data mining techniques and a large corpus of library metadata encoded in the MARC21 standard. The corpus is comprised of clustered sets of publishers and publisher locations from the library MARC21 records found in the Platform for Open Data (POD). POD is a data aggregation project involving member institutions of the IvyPlus Library Confederation and contains seventy million MARC21 records, forty million of which are unique. The discovery of publisher entity sets described forms the basis for the streamlined description of BIBFRAME Instance entities. The result of this work includes a database of association rules and RDF editor improvements. The association rules are the basis of a prototype autosuggestion feature of BIBFRAME Instance entity description properties designed specifically to support the auto‐population of publisher entities in linked data RDF editors. [ABSTRACT FROM AUTHOR]

Web sites can maintain survive only if their users are pleased. The navigation behavior data obtained through the users is very important in order to provide better service to the users of a website. Because this data can help to overcome the deficiencies that are difficult to predict or notice, and to make websites more userfocused. In this study, which analyzes the monthly web log files of Kırklareli University Distance Education Center (KLUDEC) web site, it is aimed to determine the in-site navigation behaviors of the users and to provide suggestions for the website to serve better by going out from these findings. For this purpose, the focus was on March, 2018, when the interactions with the courses (application, registration, examinations, etc.) were intensive. One of the data mining algorithms FP-Growth (frequent patterns-growth) was used for analyzing the web log files. It has been determined that the results of the study in which certain patterns are extracted from the users' navigation behaviors show that the most interested trainings are "Microsoft Sertifikasyon", "İş Sağlığı ve Güvenliği (İSG)" and "Tezsiz Yüksek Lisans". Moreover, findings also reveal that users are interested in these trainings at the same time. In the light of all the findings, the improvement recommendation, which may be useful for both KLUDEC website and different distance education websites, are shared in the results section. [ABSTRACT FROM AUTHOR]

Rapid technological developments caused the increasing number of computerized data processing. With the increasing complexity of business processes, business process management technologies such as ERP (Enterprise Resource Planning) are increasingly being used. This resulted in the availability of data more abundant so that excavation and search information from the dataset will be a valuable knowledge. In this paper, we have done the process mining to obtain an interesting pattern of event log data. In this research, data mining method that we are used is the sequential pattern mining algorithm using FP-Growth- Prefix Span. In addition, we are also used the fuzzy approach to handle the time interval of the analyzed data, so that the sequential pattern that produced become fuzzy time-interval sequential pattern. The application of these methods in a business processes that produce fuzzy time interval sequential pattern. From the analysis, the result shown that there is a minimum effect on the pattern of the resulting support. Furthermore, the results of the analysis can be used as consideration in the analysis of business processes. [ABSTRACT FROM AUTHOR]

In view of the traditional parallel FP-growth algorithm (PFP)that suffers from two major limitations, namely, multiple database scans requirement (i.e., high I/O cost) and high inter-processor communications cost, therefore we design and implement a parallel association rules mining method based on cloud computing. The algorithm adopts the separation strategy to simply visit a local database only once, thus, the inter-processor communication I/O overhead is reduced. What's more, the MapReduce model is used to solve the problem of huge amounts of data mining, as well as the calculated execution taking place in the local data storage node, which can avoid large amounts of data on the network transmission and reduce the communication overhead. By using ordinary PC structures, Hadoop cluster experimental results verify that the proposed algorithm based on cloud computing offers higher efficiency and has a good speedup. [ABSTRACT FROM PUBLISHER]

In the single machine environment, the problems of Apriori and FP-Growth algorithm in large-scale data association rules mining are high memory consumption, low computing performance, poor scalability and reliability and so on. Therefore, we put forward a new implementation method which is based on MapReduce parallel environment for mining frequent itemsets to generate association rules and is verified by using different sizes of real datasets with different nodes in the cluster, meanwhile, selecting “speedup, scalability and reliability” as an indicator. The results show that our method is feasible and valid and is able to improve the overall performance and efficiency of Apriori and FP-Growth algorithm to meet the needs of large-scale data association rules mining. [ABSTRACT FROM PUBLISHER]

There is potential knowledge inherent in vast amounts of untapped and possibly valuable data generated by healthcare providers. So often, clinicians rely in their skills and experience and that of other medical experts as their source of information. The healthcare sector is now capturing more data in the form of digital and non digital format that may potentially be mined to generate valuable insights. In this paper we propose a five step knowledge discovery model to discover patterns in medical audiology records. We use frequent pattern growth (FP-Growth) algorithm in the data processing step to build the FP-tree data structure and mine it for frequents itemsets. Our aim is to discover interesting itemsets that shows connection between hearing thresholds in pure-tone audiometric data and symptoms from diagnosis and other attributes in the medical records. The experimental results are summaries of frequent structures in the data that contains symptoms of tinnitus, vertigo and giddiness with threshold values and other information like gender. [ABSTRACT FROM PUBLISHER]

Business Intelligence is a broad category of applications which includes gathering, storing and analyzing business data to make better business decisions. The aim of this tool is to help an application engineer to select the product by analyzing the standard product component database and to observe the frequent patterns of client's requirements. Approach is to use the frequent pattern mining to enable an application engineer to analyze the standard product component database for detecting the frequent requirements made by the client. The approach used for the frequent pattern mining is divide and conquer to mine the frequent patterns from the product component database recursively. [ABSTRACT FROM PUBLISHER]

Due to prevalent use of sensors and network monitoring tools, big volumes of data or “big data” today traverse the enterprise data processing pipelines in a streaming fashion. While some companies prefer to deploy their data processing infrastructures and services as private clouds, others completely outsource these services to public clouds. In either case, attempting to store the data first for subsequent analysis creates additional resource costs and unwanted delays in obtaining actionable information. As a result, enterprises increasingly employ data or event stream processing systems and further want to extend them with complex online analytic and mining capabilities. In this paper, we present implementation details for doing both correlation analysis and association rule mining (ARM) over streams. Specifically, we implement Pearson-Product Moment Correlation for analytics and Apriori & FPGrowth algorithms for stream mining inside a popular event stream processing engine called Esper. As a unique contribution, we conduct experiments and present performance results of these new tools with different tumbling and sliding time-windows over two different stream types: one for moving bus trajectories and another for web logs from a music site. We find that while tumbling windows may be more preferable for performance in certain applications, sliding windows can provide additional benefits with rule mining. We hope that our findings can shed light on the design of other cloud analytics systems. [ABSTRACT FROM PUBLISHER]

In data mining, the association rules are used to search for the relations of items of the transactions database. Following the data collected and stored, it can find values through association rules, and assist manager to proceed marketing strategies and plan market framework. In this paper, we attempt to use fuzzy partition method and decide membership function of quantitative values of each transaction item. Also, from managers we can reflect the importance of items as linguistic terms, which are transformed as fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth is used to complete the process of data mining. The method above is expected to improve Apriori algorithm for its better efficiency of the whole association rules. An example is given to clearly illustrate the proposed approach. Finally, the experiment results are made to show the performance of the proposed methods. [ABSTRACT FROM PUBLISHER]

Nowadays the database of an organization is increasing day by day. Sometimes it is necessary to know the behavior of that organization by retrieving the relationships among different attributes of their database. Implication of association rules provides an efficient way of data mining task which is used to find out the relationships among the items or the attributes of a database. This paper addresses on implication of association rules among the quantitative and categorical attributes of a database employing classical logic and Frequent Pattern (FP) - Growth algorithm. The system is based on generating association rules over binary or categorical attributes and is organized with splitting the quantitative attributes into two or more intervals to generate association rules when the domain of quantitative attribute increases. The effectiveness of the method has been justified over a sample database. [ABSTRACT FROM PUBLISHER]

In this paper, the basic characteristic of web access information puts forward the improved data mining algorithm in the application of e-commerce industry. Deep analysis of web access to information, it comes from solving real problem from the design of web based access information mining application model. It puts forward several key technical problem methods. for solving the problems. [ABSTRACT FROM PUBLISHER]

Association rules produced by the Apriori and FPGrowth algorithms reveal demographic factors associated with increased incidence of liver cancer and may provide a mechanism for early detection. [ABSTRACT FROM PUBLISHER]