1. INTRODUCTION Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Multimedia data mining refers to the analysis of large amounts of multimedia information in order to find patterns or statistical relationships. Once data is collected, computer programs are used to analyze it and look for meaningful connections. This information is often used by governments to improve social systems. It can also be used in marketing to discover consumer habits. Deep learners are a type of Artificial neural networks. They have multiple data processing layers that learn representations by increasing the level of abstraction from one layer to the next. These methods have accomplished the state-of-the-art in multimedia mining, in speech recognition, visual object recognition, natural language processing and other areas such as genome mining and predicting the efficacy of drug molecules. Here I describe some of the Deep Learning Techniques used for multimedia data
1) Data mining is a way for companies to develop business intelligence from their data to gain a better understanding of their customers and operations and to solve complex organizational problems.
To begin with, Dell software an information technology enterprises describes Data Mining as “an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the
Data mining is essentially the ability to discover new information by exploring through various databases of existing information. According to Laura and Jack Cook, data mining "facilitates the discovery of previously unknown relationships among the data. …These operations present results that users already intuitively knew existed in the database."[2] As an example, let us take a school system consisting of three databases: one which stores the student profiles consisting of name and identification number, another to store student grades based on identification number, and the last one stores all the transactions at the bookstore through the student identification card. This is a simple example, but it should illustrate our point. Alone, the separate databases might not tell us much. With data mining techniques, the process might be able to tell us that in a particular school year, students of a certain ethnic background obtained above a 3.0 GPA, or that the bookstore sold mostly engineering books to students last year, or even that students who obtained above a 3.0 GPA were ones who bought engineering books. More specifically, the technology might be smart enough to associate that John Doe from Ireland had a 3.32 GPA in his engineering classes, even though he did not buy any engineering books from the bookstore. This type of technology is very powerful source of
Data analysis is a procedure of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. There are multiple facts and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains in data analysis. For data analysis we have to mine the data first for our purpose such that the data we can handle easily. Basically for data analysis our first thing to do our planning, how we are going to collect the data, our going data going to make sense or not, actually data will be meaningful for our object, after
Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.
Data mining is another feature that is used in spreadsheets to manipulate complex data. Data mining is a feature that allows user to extract specific features such as filtering the names of students by surname. An example is if Ridgeway College needed to find contact details of students enrolled in 2016 all they would have to do is to filter the years and type in 2016, this will
Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.
What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,
Data mining is really just the next step in the process of analyzing data. Instead of getting queries on standard or user-specified relationships, data mining goes a step farther by finding meaningful relationships in data. Relationships that were thought to have not existed, or ones that give a more insightful view of the
Data Mining, a sub-branch of computer science, involving statistics, methods and calculations to find patterns in large amount of data sets, and database systems. Generally, data mining is the process to examine data from different aspects and summarizing it into meaningful information. Data mining techniques depict actions and future trends, allowing any individual to make better and knowledge-driven decisions.[1][2]
The definition of data mining is seemingly a harmless one: it is essentially the gathering of data from different perspectives and gathering it into functional purposes. Similar to coal or oil
Data analytics includes the process of the analysis after collecting, of data to determine patterns as well as all types of information. Businesses profit from this data analysis and how it has been classified.
Data mining is the procedure of getting new patterns from large amount of data. Data mining is a procedure of finding of beneficial information and patterns from huge data. It is also called as knowledge discovery method, knowledge mining from data, knowledge extraction or data/ pattern analysis. The main goal from data mining is to get patterns that were already unknown. The useful of these patterns are found they can be used to make certain decisions for development of their businesses. Data mining aims to discover implicit, already unknown, and potentially useful information that is embedded in data.
Data Mining is a collection of number of computational approaches. These approaches are used to develop
This report is divided into three task in context to data analysis and data mining. The first task consist of rapid miner and it uses data analysis in order to get the details of the customer. The first part explains the factors effecting the deliquesces. This analysis helps in understanding the data of customer. After all this analysis is done then exploratory analysis is done this is done using rapid miner. This variable are used for making decision tree and logistic regression model which gives the analyst a predictor variables. The next step is making a report on data warehouse and security concerns around it. The last step is tableau software which is being used to manufacturing for San francisco police department.