This event would lay a platform for all the Academicians, Data Base engineers, Scientists/Researchers, Professors, President/Vice president, Chairs/Directors, Data Scientists, Students, Experts and Delegates to interact and intend their advanced scientific researches with global eminent scientists and accelerate progress in Data mining
Event Description: The Data Mining 2018 conference focuses on the topics Big Data Analytics, Data Warehousing, Data Mining Methods and Algorithms , Data Mining Tasks , Big Data Technology, Business Analytics & Data Warehousing, Data Mining Tools and Software’s , Clustering , Cloud Computing for big data Task Mining and Process , Data Mining Analysis, Data Mining and Machine Learning , Optimization and Frequent Pattern Mining , New Visualizing Techniques and Data Mining Applications in Science, Engineering, Healthcare and Medicine. By bringing together interdisciplinary researchers working in various fields. This conference would be a great opportunity for the global scientists with great mark of vision in the field of big data and data mining.
Data mining methods and algorithms an interdisciplinary sub-field of computer science is the computational process of discovering patterns in large data sets involving methods like Big Data Search and Mining, Novel Theoretical Models for Big Data, New Computational Models for Big Data, High performance data mining algorithms, Methodologies on large-scale data mining, Methodologies on large-scale data mining, Big Data Analysis, Data Mining Analytics, Data Mining in Healthcare Big Data and Analytics. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre- processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. This task can be specified in the form of a data mining query. A data mining query is defined in terms of data mining task primitives. This track includes Competitive analysis of mining algorithms, Computational Modeling and Data Integration, Semantic-based Data Mining and Data Pre-processing, Mining on data streams, Graph and sub-graph mining, Scalable data preprocessing and cleaning techniques, Statistical Methods in Data Mining, Data Mining Predictive Analytics. Data Mining tools and softwares include Big Data Security and Privacy, E-commerce and Web services, Medical informatics, Visualization Analytics for Big Data, Predictive Analytics in Machine Learning and Data Mining, Interface to Database Systems and Software Systems. In computing, a data warehouse, also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. Data Warehousing are central repositories of integrated data from one or more disparate sources. This data warehousing includes Data Warehouse Architectures, Case studies: Data Warehousing Systems, Data warehousing in Business Intelligence, Role of Hadoop in Business Intelligence and Data Warehousing, Commercial applications of Data Warehousing, Computational EDA (Exploratory Data Analysis) Techniques, Machine Learning and Data Mining. Big data mining is the process of analysing data from different vision and abstracting it into useful information -information that can be used to generate more profit, reduce costs, or both. Data mining software comprises of analytical tools for analysing and processing data. It allows users to analyse data from many different perspective or angles, categorize it, and make an abstract of relationships identified. It seems that Hadoop, by offering lower cost distributed computing, did as much to advance Big Data as any other software solution. So certainly, any list of open source Big Data platforms will start with Hadoop. Yet as the rise of Spark shows, Hadoop may be a founding pioneer – and may well retain its place as the foundation of Big Data – but will not of course be its sole cornerstone. So think of this list (which does indeed start with Hadoop) as a glimpse of the pioneering days, the true infancy, of Big Data. The solutions on this list all look, to a greater or lesser extent, to Hadoop as a standard by which to compare their own performance. But the range of the list shows that this comparison is indeed just a springboard, and that many other open source Big Data solutions are sure to evolve in the years ahead.
Data mining structures and calculations an interdisciplinary subfield of programming building is the computational arrangement of finding case in awesome information sets including techniques like Big Data Search and Mining, Novel Theoretical Models for Big Data, High execution information mining figuring’s, Methodologies on sweeping scale information mining, Methodologies on expansive scale information mining, Big Data Analysis, Data Mining Analytics, Data Stream algorithm, Randomized algorithm for mater and data, Algorithm technique for big data analysis, Model of computing for modern algorithm table. Information Mining gadgets and programming ventures join Big Data Security and Privacy, Data Mining and Predictive Analytics in Machine Learning, Boundary Data Mining is a cross-disciplinary field that focuses on discovering properties of data sets. (Forget about it being the analysis step of “knowledge discovery in databases” KDD, this was maybe true years ago, it is not anymore). Two Techniques are using they are visualization techniques or Topological Data Analysis. On the other hand, Machine Learning is a sub-field of data science that focuses on designing algorithms that can learn from and make predictions on the data. Machine learning includes Supervised Learning and Unsupervised Learning methods. (e.g. clusters or rules). It is clear then that machine learning can be used for data mining. However, data mining can use other techniques besides or on top of machine learning. Data Science, that is competing for attention, especially with Data Mining and KDD. Even the SIGKDD group at ACM is slowly moving towards using Data Science.The basic calculations in information mining and investigation shape the premise for the developing field of information science, which incorporates robotized techniques to examine examples and models for a wide range of information, with applications extending from logical revelation to business insight and examination.