Today we will discover more about data mining. If you are not familiar with this concept, it is better that you start to understand more what is behind. We are talking about powerful tools and techique that will help you to get insight from your big data.
A simple definition is:
Data mining is the sum of technique and methodologies to collect information from different sources and manage in automatic way through algorithms and logical patterns
How data mining could help you to collect data ?
Data are growing fastly not only on social and open database, but everywhere.
With data mining tecnique like data scraping (taking data from internet, like ecommerce price , weather data, stock exchange…), you can increase number of datasources that you can use for your analysis.
Did you know that you can get data also from images. Discover more here:
https://www.datacamp.com/community/tutorials/datasets-for-images
In few minutes with very small line of code you can learn how to web scraping data using Python and R
How to group your data: clustering analysis
Image a big databases with many customers. It often happen that you have a lot of different groups of customer . Clustering analysis could easily identify which are customer with affinity that you can address in a similar group target, maybe because they are similar to size order, purchase need, purchase attitude.
This will help you or your firm to set different pricing, product and general marketing strategies more focused for that particular target.
Using Python or R will help you to identify clusters (see below an example of 3 clusters)
Other examples could be find here:
Regression analysis: identify future output based on historical data
Consider a dataset with icecream sales of last three years and one with temperature information. With regression you can create an algorithm to estimate how much icecream you can sales based on expected temperature
Interesting article that clarify more regression, expecially on marketing
Anomaly detection
Yes, how many times you have seen dataset with errors like typo distraction or duplicated info. Through specific tools and Machine learning you can easily identify and prevent this kind of error analyzing historical data and suggesting correct value.
How much time you can save from more robust and clear data set? Data scientist usually pass from 70% to 90% cleaning data
Classification analysis: a powerful data mining technique
In this field are growing machine learning algorithm and chatbot that in future could try to solve most of our questions, maybe about a product features, classifying our question base on common patterns.
Could be also interesting to identify common words in books, text, maybe through Wordcloud.
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