DATA MINING - CHARACTERISTICS and APPLICATION

Big data involves analyzing large and complex datasets that cannot be processed by traditional methods. It faces challenges including data volume, variety, velocity, and veracity. Data visualization helps address these challenges by making patterns in the data easier to see. It allows faster understanding of data and trends. Effective visualization techniques depend on the type of data, and may include standard charts, geometric transformations, icons, pixels, hierarchies, tags, clusters, motion charts, dashboards, color/size/connections, maps, and text analysis.

Data science involves analyzing structured, semi-structured, and unstructured data to extract knowledge and insights. It employs techniques from fields like statistics, computer science, and information science. Data scientists possess strong skills in programming, statistics, data modeling, and machine learning. The data processing lifecycle involves data acquisition, analysis, curation, storage, and exploration. Big data is characterized by its volume, velocity, and variety. Technologies like Hadoop use clustered computing and distributed storage like HDFS to efficiently process and store large amounts of structured and unstructured data.

Third lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/). Lecturer: Lauri Eloranta Questions & Comments: https://twitter.com/laurieloranta

In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. 1 Data mining is an interdisciplinary sub field of computer science and statistics with an overall goal to extract from a data set and transform the information into a comprehensible structure for further use. 1 2 3 4 The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as 'knowledge discovery' in databases, the term data mining wasn't coined until the 1990s. What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power. Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Rupashi Koul "Overview of Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31368.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31368/overview-of-data-mining/rupashi-koul

The document discusses the field of data mining. It begins by defining data mining and describing its branches including classification, clustering, and association rule mining. It then discusses the growth of data in various domains that has created opportunities for data mining applications. The document outlines the history and development of data mining from empirical science to computational science to data science. It provides examples of data mining applications in various domains like healthcare, energy, climate science, and agriculture. Finally, it discusses future directions and challenges for the field of data mining.

This document provides an introduction to data mining techniques. It discusses how data mining emerged due to the problem of data explosion and the need to extract knowledge from large datasets. It describes data mining as an interdisciplinary field that involves methods from artificial intelligence, machine learning, statistics, and databases. It also summarizes some common data mining frameworks and processes like KDD, CRISP-DM and SEMMA.

The document provides an overview of key concepts in data science and big data including: 1) It defines data science, data scientists, and their roles in extracting insights from structured, semi-structured, and unstructured data. 2) It explains different data types like structured, semi-structured, unstructured and their characteristics from a data analytics perspective. 3) It describes the data value chain involving data acquisition, analysis, curation, storage, and usage to generate value from data. 4) It introduces concepts in big data like the 3V's of volume, velocity and variety, and technologies like Hadoop and its ecosystem that are used for distributed processing of large datasets.

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