Techniques that can be used for Data Mining

In order to gain valuable business insights, immeasurable amount of data is produced, collected and stored by companies. Data mining dates back to the 1930's and it’s all about screening and sifting through this data. Through this tremendous amount of data businesses try to figure out the hidden patterns and trends and make evaluation accordingly. The need for data scientists is growing as businesses has to grapple for success in the fast paced industry and fact finding is thus essential.

There are several techniques involved in data mining that aspiring data scientists and businesses should learn and know. These methods are listed and thoroughly explained below:

1. Clustering: It is the visual representation of data in the form of graphs. It depicts the sales over a period of time for a specific product, business buying trends or customer’s demographics etc. It is done by grouping together a set of data points according to some characteristics. In this way, the data mining specialists are able to discern and make pragmatic decision because data is separated into subsets on the basis of consumer behavior and demographics. Clustering technique is implemented almost everywhere. For example in grocery or retail stores it gives a better idea to the marketers that which customer purchases which products and in how much quantity per month. They can manage their inventory, replenish items and target specific geographical areas or consumer segments based on this information. Similarly, companies that operate online and have brick and mortar stores can use clusters to rationally plan and manage their staff or timings by evaluating and analyzing whether they are more active virtually (through their apps) or make purchases my visiting stores.

2. Association process: In this model data mining professionals find and depict intriguing relationships or correlations between two items from large data sets also known as variables. It prepares companies for further market research and helps them to come up with new strategies. For example if you visit an electronic shop or a website and buy a smartphone you will get suggestions related to closely related items (complementary goods). In this case it can be screen protector or phone cover because it is likely that you would end up buying it. 

3. Data cleansing: Here the errors are eliminated, superfluous, corrupted or redundant data is eradicated, and later arranged. The null values are replaced and only the useful data or information is extracted and processed for further analysis. Data cleansing is important because incorrect and massive amount of unnecessary data can lead to errors and faults is the process of analysis. Along with this, it requires a lot of time and resources that increases costs for businesses.

4. Visualization of Data: For better understanding and to give a clear picture to all the business stakeholders of the findings, this data is visually transformed by data miners into graphs, maps, 3D models, charts, figures and diagrams etc. This makes decision making faster and more efficient.

5. Predictions: In order to take best course of action and to make accurate future predictions large data sets are required. If companies have large sample size and historical data that means best business projections will be made.

6. Data warehousing: This is another technique in which data is gathered and stored before it is used for analysis and evaluation. This is done prior to the data mining process and it helps to segment and target customers according to the number of purchases made, registration for loyalty programs or discount vouchers availed etc.

There are many other techniques involved in the data mining such as machine learning and neural networks (computer systems make human like decisions), classification and even detection of outliers. Data mining is vital for companies that are seeking for competitive edge or looking for means to increase their productivity so they must invest in it.