Data mining uses computing power and algorithms to find patterns and connections in large databases. It is used in corporate trends, decision support systems, and anti-terrorism efforts. Techniques include regression, Bayesian inference, and decision trees. Data visualization is important for presenting findings. As data becomes more abundant, data mining will become increasingly important.
Data mining uses a relatively large amount of computing power operating on a large data set to determine the regularities and connections between data points. Algorithms employing techniques from statistics, machine learning and pattern recognition are used to automatically search large databases. Data mining is also known as Knowledge-Discovery in Databases (KDD).
Like the term artificial intelligence, data mining is an umbrella term that can be applied to a number of different tasks. In the corporate world, data mining is used more frequently to determine the direction of trends and predict the future. It is used to build models and decision support systems that provide people with information they can use. Data mining takes a leading role in the battle against terrorism. It is supposed to have been used to determine the leader of the 9/9 attacks.
Data miners are statisticians using techniques with names like near-neighbor models, k-means clustering, holdout method, k-fold cross validation, leave-one-out method, and so on. Regression techniques are used to subtract irrelevant patterns, leaving only useful information. The term Bayesian is seen frequently in the field, referring to a class of inference techniques that predict the probability of future events by combining prior probabilities and probabilities based on conditional events. Spam filtering is probably a form of data mining, automatically bringing relevant messages to the surface from a chaotic sea of phishing attempts and Viagra offers.
Decision trees are used to filter mountains of data. In a decision tree, all data passes through an input node, where it is faced with a filter that separates the data into streams according to its characteristics. For example, consumer behavior data is likely to be filtered based on demographic factors. Data mining isn’t primarily about fancy graphs and visualization techniques, it’s about employing them to show what it has found. It is known that we can absorb more statistical information visually than verbally and this presentation format can be very persuasive and powerful when used in the right context.
As our civilization becomes increasingly saturated with data and sensors are deployed en masse in our local environments, we will inadvertently discover things that may be missing on the first pass. Data mining will allow us to fix these mistakes and discover new insights based on past data, giving us more money for our data storage.
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