Data mining is effectively storing and analysing old pieces of data and predicting what's going to happened in future based on trends and patterns in that data.
They could be trends.
There are a few ways to organize data and reveal trends. You will have to set a plan, budget and people.
There are two main purposes. One purpose is for pattern recognition. Humans are very good at recognising patterns such as [linear] trends, cyclical patterns, clusters and so on. This is useful for preliminary investigation of data.The second purpose is to illustrate summary data in a quick-and-easy way.
They help you identify patterns in the data.
I think they look for trends or patterns in the data.
To analyze information for patterns and trends, start by organizing the data and identifying key variables. Use statistical techniques like correlation analysis, regression analysis, and data visualization tools to spot patterns. Look out for recurring themes, anomalies, or relationships between variables to uncover trends in the data.
Data-driven insights are what a person gets from analyzing data for patterns and trends, giving insight into what is to be done.
Data from Research
I like to analyze data as a scientist. there is your sentence
Data mining is effectively storing and analysing old pieces of data and predicting what's going to happened in future based on trends and patterns in that data.
Synthesis and analysis
synthesis and analysis
The purpose of a tool that monitors trends is to locate specific trends in areas. It is also to locate patterns among the trends. By doing so, scientists or analysts can find something similar among the trends and use that as data for their research.
One advantage of graphing daa in general is to see patterns. Many types of graphs will show patterns. Line graphs are excellent for seeing patterns and trends.
The study of predictable patterns is called pattern recognition. It involves identifying regularities or trends in data to make informed predictions or decisions.
Graphs can reveal patterns, trends, and relationships in data that might not be evident from simply looking at the raw numbers. They can help to visualize data, identify outliers, and make comparisons between different data sets more easily. Additionally, graphs can provide insights into the distribution and shape of data, as well as aid in detecting any potential correlations or causal relationships.