- "Pattern recognition is the automated recognition of patterns and regularities in data."
The process of identifying and analyzing patterns in intelligence data that could suggest a threat or provide insights into a situation.
Pattern recognition: The ability to identify similarities, differences and relationships between different data sets or pieces of information.
Data analysis: The process of examining data sets to identify patterns, insights and correlations that can help in decision-making.
Data visualization: The use of charts, graphs, and other visual aids to represent data in a more intuitive and understandable way.
Statistical analysis: The use of statistical methods to help draw conclusions and identify patterns in complex data sets.
Machine learning: The use of machine algorithms to identify patterns and relationships in large amounts of data.
Natural language processing: The ability to understand and analyze human language and communicate with people through language.
Geographic information systems (GIS): The use of spatial data to analyze and represent patterns and relationships.
Text mining: The use of computational techniques to extract useful information from large amounts of text data.
Image processing: The use of algorithms to analyze, transform, and enhance images.
Data modeling: The creation of mathematical models to represent real-world phenomena and help identify patterns and relationships.
Signal processing: The use of techniques to process and analyze signals from different sources, such as audio or video data.
Network analysis: The study of relationships and connections between different entities, such as people or organizations, to identify patterns and insights.
Data mining: The extraction of useful insights and patterns from large amounts of data using computational techniques.
Cognitive psychology: The study of mental processes, such as perception and attention, and how they affect pattern analysis and decision-making.
Human factors: The study of how people interact with systems and how these interactions affect the success of a pattern analysis project.
Trend analysis: This involves identifying and analyzing patterns in data over time to identify trends that could be useful in predicting future events or patterns.
Link analysis: This is the process of identifying connections or relationships between different entities such as individuals, groups, organizations, or events. This analysis helps to reveal how different people, groups or events related to each other.
Network analysis: This type of analysis is focused on identifying the structure of networks and the role of different entities within those networks. For instance, analyzing elements such as communication, coordination or resources exchange to classify, for example, the members of an organization.
Cluster analysis: This involves using a set of algorithms to group similar data together according to their characteristics. This analysis helps to improve decision making by identifying patterns and trends in data.
Geographic Information System (GIS): A GIS technology is used to capture and analyze spatial data, such as locations of people, places or events. The analysis helps to identify relationships or patterns that are associated with the geographic location of data.
Time-series analysis: This analysis technique applies statistical methods to time-related data points, such as stock information or weather data, to identify significant trends or patterns in the data.
Behavioral analysis: This analysis technique involves studying the behavior of individuals or groups, especially those that are likely to be associated with hostile activities, in order to anticipate and prevent such activities.
Text analysis: This is the analysis of unstructured data such as text documents, emails, or social media posts, to extract relevant information for further analysis.
Pattern recognition: This involves using algorithms and machine learning to identify patterns in large datasets that may not be immediately obvious to human analysts.
Image analysis: This is the analysis of visual images such as satellite images or aerial photographs, to identify patterns or trends that may not be noticeable to the naked eye.
- "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 pattern."
- "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."
- "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 a stronger connection to business use. Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration."
- "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."
- "An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is 'spam')."
- "Pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, sequence labeling, and parsing."
- "Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform 'most likely' matching of the inputs, taking into account their statistical variation."
- "This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns."
- "A common example of a pattern-matching algorithm is regular expression matching."
- "Regular expression matching looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors."
- "Pattern recognition 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."
- "Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration."
- "PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning."
- "Pattern recognition systems are commonly trained from labeled 'training' data."
- "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."
- "A leading computer vision conference is named Conference on Computer Vision and Pattern Recognition."