Introduction To Data Mining:
Data mining has introduced the creation of functional data that is then used by different departments in different ways. The structure of this data varies depending on the department that will be using it. It can range anywhere from a list of emails (that the marketing department sends out promotional messages), to JSON data (that could use to create predictive models for business intelligence).
When it comes to data structures, there are many of them. Some of the most used non-primitive ones being arrays, stacks, queues, linked lists, trees, graphs, and hash tables. When mining data, it is important to stick to the data-structure required for solving the business statement as otherwise. There will be a need for conversion of data type before consuming. Sometimes data may also store in binary formats, which are not human-readable. An example of this is predictive models generated by engines such as Tensorflow.
The use of different data structures in various departments and sectors:
As we mentioned, a single data structure does not allow many functionalities and each has its benefits and limitations. The data types we will discuss here built upon primitive data types such as numbers and string. Here are some used data types and ways in which they use in businesses-
1. An array or a list is one of the most popular data types of many purposes wherever a list of items required. Whether you need to store a mailing list, keep a record of the names of all the employees in your company.
Or have the names of all the brands that your competitors are stocking, you can use an array. It contains a list of strings or numbers and maintains the order of the data. For example, if an array looks like – [“Nike”, “Adidas”, “Puma”, “Reebok”], then this order of data will maintain unless changed.
2. Another popular, but more complicated data structure is the graph. This data structure can store tons of information that connect. If you are running an eCommerce website, and you want to build a recommender system using the data at hand through data mining.
Chances are that you will be generating a graph data set. That will tell you which products associated with any product and that way you can recommend items to customers.
Fig: A Graph Data Structure
Data Mining Graphs used by various tech giants to provide us with services every day:
1. Google maps use the data structure to build their maps- where an intersection of roads serves as a node while the roads themselves serve as edges. Then, an algorithm used to find the shortest path between two nodes when someone uses their app.
2. In Facebook, users will save as nodes, and when two users are friends, then there’s a line connecting them. The “people you may know” suggestion algorithm uses this data structure to suggest probable friends to you.
3. Even the World Wide Web that you use everyday uses graph data structure. The pages considered to be the nodes and there’s a connection between two nodes if there’s a link of one page in another.
Maps, also called dictionaries, hashmaps, or JSON, is a data structure used to save chunks of information. For example, when a website wants to save customer information, it will usually save in such a format.
“name”: “John Doe”,
Such data-structures used when data needs to share between two systems. For example, say your marketing department needs the data for all the customers in California. Then the IT department will share an array of such JSON. The marketing department will then analyze specific data-points and decide whom to target for their new offers.
4. A queue, which is a form of a first-in-first-out data structure in most cases where a continuous feed of information needs to consume. And information that arrives first needs to processing first. For example, say you are consuming a continuous job-data feed from JobsPikr. You will need to make sure the data stored in a queue and then consumed by your system.
Why is that?
Well, a job posting that comes in first needs posting first. So to maintain ordering and to have the latest jobs at the top. The need for such ordered data-structure felt across different systems in an organization. Where data needs processing based on their timing.
5. Deque is a data structure like a queue, but having a specific use case. The best example of a deque would be your computer’s history. Pages browsed last added to the top of the queue while those at the end removed after some time.
Coming to business standpoints, you may save the latest orders on your website in a deque. Ones that occurred long back removed after a given period, whereas those which are the latest need to be at the top for analysis.
Why is data structure so important when mining data?
If you save data in a data structure that does not suit the type of action that you will be taking based on the data. There can be many problems that can occur- your processes might become slow and the massive time lag might make the solutions unfeasible. For example, if someone uses data mining to create a predictive model.
But the model takes a few minutes to provide recommendations to users, then almost no customer will be able to use it when browsing the website. The behavior of a system might change if the data structures used are wrong. For example, you may have a notification engine that should be sending the latest notification it receives. If you end up using a non-FIFO queue. Data integrity will not maintain and you may end up sending data that arrived later before data that arrived.
Data mining and web scraping go hand-in-hand when it comes to building large data processing systems. This helps businesses in making the right decisions. Through DataStock, we provide users with ready-to-use datasets from different industries. Like recruitment, eCommerce, retail, and more. Companies use these datasets to test machine learning algorithms.
They also spot trends, analyze sentiments, and use natural language processing on raw data. You can download a free sample for any dataset that you want to buy, and each dataset. It is in a ready-to-consume CSV format so that no time wasted for you to process the data.