With the development of science and technology and the popularization of the Internet, more and more data are available to people, and most of these data exist in the form of text. Most of these text data are complex, which leads to a situation where the amount of data is large but the information is relatively scarce. How to obtain useful information from these complex text data has attracted more and more attention.
Data mining can be simply understood as discovering useful knowledge from data through the manipulation of environmental data. It is an interdisciplinary subject involving a wide range, including machine learning, mathematical statistics, neural networks, databases, pattern recognition, rough sets, fuzzy mathematics and other related technologies. In terms of specific applications, data mining is a process of using various analytical tools to discover models and relationships in massive data that can be used to make predictions.
Text classification is an important application aspect of Text Mining. Text mining is derived from data mining. Data mining is the process of extracting hidden, unknown, but potentially useful information and knowledge from a large amount of incomplete, noisy, fuzzy, and random practical application data. Therefore, data mining is also embodied in the process of finding patterns in some sets of facts or observations and providing decision support.
Lingjiu Software NLPIR Big Data Semantic Intelligent Analysis Platform is aimed at the comprehensive needs of big data content acquisition, editing, mining and searching, and integrates the research results of precise network acquisition, natural language understanding, text mining and semantic search. It has served 400,000 people around the world for 18 years. It is a powerful tool for semantic intelligent analysis in the era of big data.
Lingjiu Software NLPIR big data semantic intelligent mining platform, in response to the needs of big data content processing, integrates the technologies of precise network acquisition, natural language understanding, text mining and network search, and provides client tools, cloud services, and secondary development interfaces. .
Lingjiu software NLPIR can meet the needs of users for processing big data texts from all angles, including the complete technical chain of big data: web crawling, text extraction, Chinese and English word segmentation, part-of-speech tagging, entity extraction, word frequency statistics, keywords Extraction, Semantic Information Extraction, Text Classification, Sentiment Analysis, Semantic Depth Extension, Traditional and Simple Coding Conversion, Automatic Phonetics, Text Clustering, etc.
Lingjiu Software NLPIR big data semantic intelligent analysis platform can make proactive, knowledge-based decisions by predicting future trends and behaviors. The goal of data mining is to discover implicit and meaningful knowledge from the database, which mainly has the following five functions.
(1) Automatically predict trends and behaviors: Data mining automatically finds predictive information in large databases. Problems that used to require a lot of manual analysis can now be quickly and directly concluded from the data itself.
(2) Association analysis: Data association is a kind of important discoverable knowledge that exists in the database. If there is some regularity between the values of two or more variables, it is called an association. Associations can be divided into simple associations, temporal associations, and causal associations. The purpose of association analysis is to find hidden associations in the database. Sometimes the association function of the data in the database is not known, and even if it is known, it is uncertain, so the rules generated by association analysis have credibility.
(3) Clustering: The records in the database can be divided into a series of meaningful subsets, namely clusters. Clustering enhances awareness of objective reality and is a prerequisite for conceptual description and bias analysis. Clustering technology mainly includes traditional pattern recognition methods and mathematical taxonomy
(4) Concept description: Concept description is to describe the connotation of a certain type of object and summarize the relevant characteristics of this type of object. Concept description is divided into characteristic description and distinguishing description. The former describes the common characteristics of certain types of objects, and the latter describes the differences between different types of objects. Generating a characteristic description of a class involves only the commonalities of all objects in the class. There are many methods for generating discriminative descriptions, such as decision tree method, genetic algorithm, etc.
(5) Deviation detection: The data in the database often has some abnormal records, and it is very meaningful to detect these deviations from the database. Bias includes a lot of underlying knowledge, such as abnormal instances in the classification, special cases that do not meet the rules, deviations of observations from model predictions, and changes in magnitudes over time. The basic approach to bias detection is to look for meaningful differences between observations and reference values
Data mining technology is a new network technology in recent years, but its wide application is loved by many companies and researchers. Over the years, with the passage of time and the continuous development of network technology, big data mining technology has been continuously updated and developed, and has been widely used in finance, management, teaching and other industries. I believe that with the continuous development of network technology, the application of big data mining technology will become more and more extensive.