Definition : In simple words, data mining is defined as a process used to extract usable data from a larger set of any raw data . It implies analysing data patterns in large batches of data using one or more software. Data mining is also known as Knowledge Discovery in Data (KDD).
Data mining , knowledge discovery, or predictive analysis – all of these terms mean one and the same. Broken down into simpler words, these terms refer to a set of techniques for discovering patterns in a large dataset.
Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data , businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs.
For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.
Data mining has several types, including pictorial data mining, text mining, social media mining, web mining, and audio and video mining amongst others. Read: Data Mining vs Machine Learning. Learn more: Association Rule Mining. Check out: Difference between Data Science and Data Mining. Read: Data Mining Project Ideas.
Data Mining is primarily used today by companies with a strong consumer focus — retail, financial, communication, and marketing organizations, to “drill down” into their transactional data and determine pricing, customer preferences and product positioning, impact on sales, customer satisfaction and corporate profits.
Data Mining in Banking Banks use data mining to better understand market risks. It is most often used in banking to determine the likelihood of a loan being repaid by the borrower. It is also used commonly to detect financial fraud.
Data mining is a five -step process: Identifying the source information. Picking the data points that need to be analyzed. Extracting the relevant information from the data. Identifying the key values from the extracted data set. Interpreting and reporting the results.
Data mining helps to extract information from huge sets of data . It is the procedure of mining knowledge from data . Important Data mining techniques are Classification, clustering, Regression, Association rules, Outer detection, Sequential Patterns, and prediction.
Data cleaning and preparation. Data cleaning and preparation is a vital part of the data mining process. Tracking patterns. Tracking patterns is a fundamental data mining technique. Classification . Association . Outlier detection. Clustering . Regression . Prediction.
But while harnessing the power of data analytics is clearly a competitive advantage, overzealous data mining can easily backfire. As companies become experts at slicing and dicing data to reveal details as personal as mortgage defaults and heart attack risks, the threat of egregious privacy violations grows.
A data mining specialist finds the hidden information in vast stores of data , decides the value and meaning of this information, and understands how it relates to the organization. Data mining specialists use statistical software in order to analyze data and develop business solutions.
Simply put, data mining is the process that companies use to turn raw data into useful information. They utilize software to look for patterns in large batches of data so they can learn more about customers. It pulls out information from data sets and compares it to help the business make decisions.
Data Mining is an important analytic process designed to explore data . Much like the real-life process of mining diamonds or gold from the earth, the most important task in data mining is to extract non-trivial nuggets from large amounts of data .