Contents
What is referred to as data fishing?
Data dredging, sometimes referred to as “data fishing” is a data mining practice in which large volumes of data are analyzed seeking any possible relationships between data. Data dredging is sometimes described as “seeking more information from a data set than it actually contains.”
What is the difference between data processing and data mining?
Data mining analyzes static information. In other words: data that is available at the time of analysis. Process mining on the other hand looks at how the data was actually created. Process mining techniques also allow users to generate processes dynamically based on the most recent data.
What is the difference between data mining?
Difference Between Data Mining and Data Analytics Data mining is catering the data collection and deriving crude but essential insights. Data analytics then uses the data and crude hypothesis to build upon that and create a model based on the data. Data mining is a step in the process of data analytics.
What is the goal of data mining?
A goal of data mining is to explain some observed event or condition. Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
What is fishing in research?
Data fishing or dredging refers to analyses that are done without predefined research questions. There is a high likelihood of misleading findings with data fishing, which results from the play of chance following multiple statistical comparisons.
What is data mining methods?
Data mining is looking for patterns in huge data stores. The methods include tracking patterns, classification, association, outlier detection, clustering, regression, and prediction. It is easy to recognize patterns, as there can be a sudden change in the data given.
What’s the difference between data mining and statistics?
The difference between statistics and data mining is largely a historical one, since they came from different traditions: statistics and computer science. Data mining grew in parallel out of work in the area of artificial intelligence and statistics. Section 1.4 from Witten & Frank summarizes my viewpoint so I’m going to quote it at length:
What’s the difference between data fishing and data dredging?
Data dredging, sometimes referred to as “data fishing” is a data mining practice in which large volumes of data are analyzed seeking any possible relationships between data. The traditional scientific method, in contrast, begins with a hypothesis and follows with an examination of the data.
Why is data fishing and data mining not frowned upon?
If done correctly, data-mining is not frowned upon because it is acknowledged that you are doing this to generate interesting hypotheses to be tested later, where as data-fishing implies that the researcher did not confirm the final hypothesis they inspected in a new data set. Thanks for contributing an answer to Cross Validated!
What is the difference between data mining and data warehousing?
The integration layer is used in integration of data and to have an abstraction level from users of the data. Lastly, the access layer is important in getting data out of different users of data. Both data mining and data warehousing can be referred to as tools that are used for the collection of business intelligence.