Data mining in the context of "Data analysis"

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges). The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena."

An information silo, or a group of such silos, is an insular management system in which one information system or subsystem is incapable of reciprocal operation with others that are, or should be, related. Thus information is not adequately shared but rather remains sequestered within each system or subsystem, figuratively trapped within a container as grain is trapped within a silo: there may be much of it, and it may be stacked quite high and be freely available within those limits, but it has no effect outside them.

Information silos occur whenever a data system is incompatible, or not integrated, with other data systems. This incompatibility may occur in the technical architecture, in the application architecture, or in the data architecture of a data system. Such data silos are proving an obstacle for businesses wishing to use data mining to make productive use of their data. However, since it has been shown that established data-modeling methods are the root cause of the data-integration problem, most data systems are at least incompatible in the data-architecture layer.

Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their primary function is to distinguish and create emergent patterns. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power.

Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration. It originated in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition.

A search engine is a software system that provides hyperlinks to web pages, and other relevant information on the Web in response to a user's query. The user enters a query in a web browser or a mobile app, and the search results are typically presented as a list of hyperlinks accompanied by textual summaries and images. Users also have the option of limiting a search to specific types of results, such as images, videos, or news.

For a search provider, its engine is part of a distributed computing system that can encompass many data centers throughout the world. The speed and accuracy of an engine's response to a query are based on a complex system of indexing that is continuously updated by automated web crawlers. This can include data mining the files and databases stored on web servers, although some content is not accessible to crawlers.

Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it.

The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection.