In today’s time, big data has become a very common term used today for everything related to data like data mining, data analysis, web mining, web harvesting, and web scraping.
Of course, a layman would get confused between these terms and may even use them interchangeably.
If you want to be well-informed and knowledgeable in the marketing industry, it is important that you have a comprehensive understanding of these terms.
Data Harvesting: What Is It?
As you can guess by the name, data harvesting means collecting information and data from online resources.
Most typically use it interchangeably with data extraction, web crawling, and web scraping.
Collecting can be considered as an agricultural term; it is implied in how you collect ripe crops from fields, which involves collection and relocation.
Commonly, data harvesting can be defined as the process of extracting valuable information and data from the target websites, transferring them into your database, and structuring them into the right format.
The first step of harvesting data is to use an automated crawler that will parse the target websites, collect the data and extract it, and export it in a structured format for later analysis.
Hence, you will not find anything like statistics, machine learning, algorithms. Instead, you will have to rely on certain programming languages like JAVA, R, Python, etc.
As a matter of fact, being accurate is the main gist of data harvesting.
You will find various tools and service providers that you can use for extracting information and data from the target websites.
One of the best tools for the job is Octoparse. This tool is considered the best choice since it offers a lot of amazing features and can easily be used by novice and experienced programmers alike.
Data Mining: What Is It?
In most cases, data mining is often confused and defined as the process to obtain information and data. There are some notable differences between collecting and mining data, even though both types are all about obtaining and extracting.
Data mining is defined as the method for discovering fact-based patterns that are generated from a large dataset.
Instead of simply gathering and interpreting the data, data mining involves a lot more disciplines like machine learning, computer sciences, and statistics.
There have been quite some harmful applications of data mining. The famous Cambridge Analytica Scandal is a great example.
They gathered the information of more than 60 million Facebook users and separated the ones who are not sure about their votes based on activities and identity on the social networking site.
Cambridge Analytica then used the ‘Psychographic Microtargeting’ method to spam inflammatory messages to get them to change their votes.
Data mining is all about finding out who the targets are, the work they do, and helping them achieve their goals. While it may sound magical, the process is quite complicated.
There are four key applications involved in data mining. Let us learn about these key applications in this section. The first step is all about the classification of the datasets.
As you can guess, data mining will classify people and things into various categories for further inspection.
For instance, a bank will create a classification model through the applications; for this, they will receive millions of applications that contain customer information like school diplomas, marital status, job titles, bank statements, etc.
You can then make use of algorithms to understand and decide the riskier applications than others. In fact, you will already understand which category the application belongs to as the customers start filling up the forms.
Regression is the method of predicting the trend between datasets based on numerical values. It can also be defined as the statistical analysis of the relationship that exists between the variables.
For instance, you will be able to predict the probability of crime in an area based on historical records.
Clustering is the method of grouping various data points based on similar values and traits. For instance, Amazon will group similar products based on the item’s functions, tags, and descriptions for its customers for easier identification.
Detection of Anomaly
As the name suggests, detection of anomaly is the process of keeping an out for abnormal behaviors or also known as outliers.
In most cases, banks make use of this method to look for suspicious and unusual transactions that do not fit normal or typical transaction activities.
Association learning is all about learning the answer to the question ‘what is the relationship between the value of two different features?’
Let us take the example of a grocery store. It is more likely that people who buy soda also buy Pringles together. One of the most popular applications of association rules is market basket analysis.
It will help the retailer identify the relationships of the products that are being consumed.
The above-mentioned applications form the backbone of data mining. Data mining is considered one of the cores of big data. In short, you can define the data mining process as Knowledge Discovery from Data (KDD).
Illuminating the concept of data science, KDD also helps discover knowledge and study research. It is a known fact that you will find data over the internet in two forms – structured and non-structured.
You will see the real magic when all the datasets have been grouped together categorically so that a pattern can be discovered. This will also help you draw patterns, detect abnormalities, and predict trends.