DATA MINING TECHNOLOGIES

Knowledge discovery and data mining software (Knowledge Discovery and Data Mining - KDD) as an interdisciplinary field emersion have been in rapid growth to merge databases, statistics, industries closely related to the desire to extract valuable information and knowledge in a volume as possible.There is a difference in understanding of "knowledge discovery" and "data mining." Discovery information (Knowledge Discovery) in the database is a process to identify patterns / templates of valid data, innovative, useful and, in the last measure, understandable.data mining, knowledge discovery, data warehouse, data mining tools, data mining applications

Data Mining in Healthcare: A Survey of Techniques and Algorithms with its Limitations and Challenges

The large amount of data in healthcare industry is a key resource to be processed and analyzed for knowledge extraction. The knowledge discovery is the process of making low-level data into high-level knowledge. Data mining is a core component of the KDD process. Data mining techniques are used in healthcare management which improve the quality and decrease the cost of healthcare services. Data mining algorithms are needed in almost every step in KDD process ranging from domain understanding to knowledge evaluation. It is necessary to identify and evaluate the most common data mining algorithms implemented in modern healthcare services. The need is for algorithms with very high accuracy as medical diagnosis is considered as a significant yet obscure task that needs to be carried out precisely and efficiently

Data Mining and Life Science: A Survey

As we are into the age of digital information, the problem of data overload emerges so worryingly ahead. Our ability to analyze and understand immense datasets wrap extreme behind our ability together and stores the data. But a new age group of computational techniques and tools is required to support the extraction of useful knowledge from the rapidly increasing volumes of data. These techniques and tools are the focus of emerging fields of Knowledge Discovery in Databases (KDD) and also called data mining. Data mining is highly noticeable in the fields like marketing, e-commerce or e-business or the fame of its use in KDD in other sectors or industries also. Among these sectors that are just discovering data mining are the fields of medicine and public health also. This research paper provides a survey of current technique of data mining/KDD for healthcare

Actionable knowledge discovery : methodologies and frameworks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this thesis, a formal and systematic view of actionable knowledge discovery (AKD for short) has been proposed from the system and microeconomy perspectives. AKD is a closed-loop optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and to deliver operationalizable business rules that can be seamlessly associated or integrated with business processes and systems. To support AKD, corresponding methodologies, frameworks and tools have been proposed with case studies in the real world to address critical challenges facing the traditional KDD and. to cater for crucially important factors surrounding real-life AKD. First, a comprehensive survey and retrospection on the existing data mining methodologies, issues and challenges in actionable knowledge discovery are reviewed. Second, a practical data mining methodology: domain driven data mining is addressed. Third, several frameworks have been proposed to support domain drivenactionable knowledge discovery. Fourth, case studies of domain-driven actionable pattern mining in stock markets and social security data are presented to demonstrate the usefulness and potential of the proposed domain driven actionable knowledge discovery. In summary, this thesis explores in detail how domain driven actionable knowledge discovery can be effectively and efficiently applied to the discovery and delivery of knowledge satisfying both technical and business concerns as well as to support smart decision-making in the real world. The issues and techniques addressed in this thesis have potential to promote the research on critical KDD challenges, and contribute to the paradigm shift from data-centered and technical significance-oriented hidden pattern mining to domain-driven and balanced actionable knowledge discovery. The proposed methodologies and frameworks are flexible, general and effective to be expanded and applied to mining real-life complex data for actionable knowledge

Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200

An agent-based hybrid framework for database mining

While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques. <br /

Knowledge Discovery using Various Multimedia Data Mining Technique

Knowledge discovery in databases (KDD) is the process of discovering positive information from a gathering of data. This generally used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior information on data sets and interpreting perfect solutions from the observed results. Knowledge Discovery in Databases is the process of finding knowledge in huge amount of data where data mining is the core of this process. Data mining can be used to understandable meaningful patterns from huge databases and these patterns may be transformed into knowledge. Multimedia data mining can be defined as the process of finding motivating patterns from media data such as audio mining , video mining, image mining and text mining that are not generally available by basic queries and associated results. It is the mining of knowledge and high level multimedia information from large multimedia database system. Multimedia data mining refers to sample discovery, rule extraction and knowledge gaining from multimedia database. In this paper, An Overview On various Multimedia Data technique is given and the main focus is given to the video Data Mining. DOI: 10.17762/ijritcc2321-8169.15035

Applying Data Mining Methods to Understand User Interactions within Learning Management Systems: Approaches and Lessons Learned

This article describes our processes for analyzing and mining the vast records of instructor and student usage data collected by a learning management system (LMS) widely used in higher education, called Canvas. Our data were drawn from over 33,000 courses taught over three years at a mid-sized public Western U.S. university. Our processes were guided by an established data mining framework, called Knowledge Discovery and Data Mining (KDD). In particular, we use the KDD framework in guiding our application of several educational data mining (EDM) methods (prediction, clustering, and data visualization) to model student and instructor Canvas usage data, and to examine the relationship between these models and student learning outcomes. We also describe challenges and lessons learned along the way

Knowledge discovery in database: A knowledge management strategic approach

Knowledge management had been a critical focus and interest in Information Technology, especially as it affects business organizations through the implementation of business intelligence and expertise.Knowledge discovery and knowledge conversion (tacit/implicit to explicit knowledge) play important roles in these aspects; through the application of technologies in the SECI model to aid knowledge management, and identifying the sources of the expertise whether in humans or physical databases serve as the basis for expertise’s knowledge management.This paper presents in detail the significances of knowledge discovery in databases (KDD) in achieving an all encompassing knowledge management strategy.This strategy must comprise of transparent and multiple interrelationships of organizational agents through shared mental maps, collaborative and distributed technologies, and solves all problem in other ways with a special focus on data mining which is also found in the KDD process. Extensive literatures were reviewed to operationalize Knowledge discovery in human and in data ware houses as its affect knowledge management, and bring to the fore the processes involved in KDD process, its applications, understanding using SECI model, possible challenges, and suggest the future research areas to solve the observed challenges