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What are the limitations of data mining?
Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.
Data mining is an interdisciplinary field, the assemblage of a set of disciplines, such as database systems, statistics, machine learning, visualization, and data science. It is depending on the data mining approach used, techniques from other disciplines may be applied, such as neural networks, fuzzy and/or rough set theory, knowledge representation, inductive logic programming, or high-performance computing.
Data Mining is similar to Data Science. It is carried out by a person, in a particular situation, on a specific data set, with an objective. This phase contains several types of services including text mining, web mining, audio and video mining, pictorial data mining, and social media mining. It is completed through software that is simple or greatly specific.
By outsourcing data mining, all the work can be done quicker with low operation costs. Specific firms can also use new technologies to save data that is impossible to find manually. There are tonnes of data available on multiple platforms, but very limited knowledge is accessible.
The major challenge is to analyze the data to extract essential data that can be used to solve an issue or for company development. There are many dynamic instruments and techniques available to mine data and discover better judgment from it.
The limitations of data mining are primarily data or personnel-related, rather than technology-related.
Data mining software are very powerful tools but they are not self-sufficient applications. It can be successful, and it requires skilled technical and analytical specialists who can structure the analysis and interpret the output that is created.
Data mining is used to obtain patterns and relationships, it does not tell the user the value or significance of these patterns. These types of determinations must be made by users.
The validity of the patterns discovered is dependent on how these are compared to real-world circumstances. For example, it can assess the validity of data mining applications designed to identify potential terrorist suspects in a large pool of individuals, the user can test the model using data that includes information about known terrorists.
Data mining can identify connections between behaviors and variables, it does not necessarily identify a causal relationship. For example, an application can identify that a pattern of behavior, such as the propensity to purchase airline tickets just shortly before the flight is scheduled to depart, is related to characteristics such as income, level of education, and internet use.
- What are the functionalities of data mining?
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- What are the areas of text mining in data mining?
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- What are the social implications of data mining?
- What are the basic concepts of data mining?
- What are the types of data mining models?
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- What are the types of mining sequence data?