I remembered in my childhood, during Halloween, I always used to get lost in the huge piles of candies and wish that I get more and more candies. Candies are the most affectionate things on the planet, and so is the data for the small and big organizations. But it’s never a good idea to lose oneself in the mound of data. The world is in love with data and every moment there is stacks of data being collected by various organization. In this era, where businesses are data-driven, data mining has gained utmost importance. It helps organizations, to obtain the right information from the huge pile of data which is increasing with the pace of 4X. As per a survey into data mining practices and requirements, “92% of respondents want to deploy advanced analytics more broadly across their organizations.”Organizations often prefer to go for data mining solutions. Data mining is a process used to extract useful information from raw and scattered data to develop a meaningful insight for further business strategies. This knowledge largely supports increasing customer loyalty, recognizing hidden profitability, and reducing client churn rates.
Why businesses are looking for Data Mining?
A generous amount of data is getting poured on the servers of the organizations every day.
It has become difficult for organizations to gain enough and relevant information from this data. They have to invest time and money in abstracting knowledge from this raw data. Data mining is the process of analyzing data from a large range of sources and collating this information into useful business intelligence. The gathered data is studied to discover interesting patterns, major market trends, anticipate future rich opportunities.
With the pace at which the data is getting collected, it has become crucial to use data mining techniques to obtain purposeful information from the data.
Important Data Mining techniques to be adopted in 2021
Now that we understand data mining isn’t an easy job, let us figure out which techniques make it more refined and reliable for data miners to achieve the desired results.
- Data Cleaning – The very first step and the most important and necessary technique, in data mining for all organizations, is Data Cleaning. Entire raw data is collected, cleaned, and formatted to make it actionable and ready for various analytical methods/tools.
- Classification – Classification is one of the most intricate data mining techniques that encourages you to stockpile different attributes together into noticeable categories. This technique is used to obtain important and relevant information about data and metadata.
The data mining framework classification can be done on the basis of different criteria;
- Based onthe type of data sources:
This classification is as per the type of data handled. For example, multimedia, spatial data, text data, time-series data, World Wide Web.
- Based onthe type of database involved:
This takes into account the type of data model involved viz. object-oriented database, transactional database, relational database, and more.
- Based onthe type of knowledge discovered:
This classification depends on data mining functionalities and the kind of knowledge discovered from different studies.
- As per the data mining techniques used:
This classification is as per the data analysis approach utilized, such as neural networks, machine learning, genetic algorithms, visualization, statistics, data warehouse-oriented or database-oriented, etc.
The classification can also take into account, the level of user interaction involved in the data mining procedure, such as query-driven systems, autonomous systems, or interactive exploratory systems
- Clustering – Clustering analysis is a data mining technique to identify similar data. This technique essentially groups large quantities of data together based on their similarities. From a machine learning point of view, clusters relate to hidden patterns, the search for clusters is unsupervised learning, and the subsequent framework represents a data concept. For example, Insurance companies can identify groups of policyholders with high average claims. Clustering can be used in marketing to segment customers based on the benefits they’ll experience when purchasing a specific product.
Describing the data by a few clusters mainly loses certain confine details, but accomplishes improvement.
- Regression – It is used to recognize the possibility of a particular variable, given the existence of other variables. Regression analysis is used to identify and analyse the relationship between variables because of the presence of the other factor. For example, we might use it to project certain costs, depending on other factors such as availability, consumer demand, and competition. Primarily it gives the exact relationship between two or more variables in the given data set.
- Association Rule Learning – Association Rule Learning is one of the best types of data mining techniques whose primary purpose is to track patterns. This data mining technique helps to discover a link between two or more items. It finds a hidden pattern in the data set. The association rule learning techniques work based on if/then statements. These statements help get an accurate association between independent data in a dataset, relational database, or other information repositories. The most commonly used measurement techniques here are;
- Lift:
This measurement technique measures the accuracy of the confidence over how often item B is purchased.
- Support:
This measurement technique measures how often multiple items are purchased and compared it to the overall dataset.
- Confidence:
This measurement technique measures how often item B is purchased when item A is purchased as well.
The main application of Association Rule Learning is in classification, data analysis, cross-marketing, catalog design, Clustering, and loss-leader analysis, etc.
- Outlier Analysis – There are certain cases where merely recognizing the overarching pattern cannot give you a clear understanding of your data set. It is equally important to ascertain anomalies or outliers in your data. It is also known as an anomaly detection technique.The outlier is a data point that diverges too much from the rest of the dataset.The main application of the Outlier detection is in social network analysis, cyber-security, distributed systems, health care, and bioinformatics.
- Sequential Patterns – This is one of the few data mining techniques that uncover a series of events in a logical sequence. It comprises of finding interesting sub-sequences in a set of sequences, where the stake of a sequence can be measured in terms of different criteria like length, occurrence frequency, etc.By comprehending sequential patterns, organizations can recommend additional items to customers to increase the sale. The main applications of Sequential patterns are in customer shopping sequence, telephone calling patterns, natural disasters, science & engineering processes, DNA sequences, medical treatments, stocks & markets,
- Prediction Analysis – It is considered as the most valuable data mining technique because it’s a process that businesses use to project their future data needs. In short, it allows brands to plan their data collection methods around gathering the right data. It uses a combination of other data mining techniques such as trends, clustering, classification, etc. It analyses past events or instances in the right sequence to predict a future event. An often-referred example of that is that you might review the customers’ credit histories and past purchases to predict whether they’ll be a credit risk in the future or not.
- Data Visualization – It gives a cutting edge to the data, by presenting information in the form of charts and graphs in real time. Thanks to the dynamic dashboards that are created using data visualization software, obtaining various insights, KPI’s and trends from data has become easier than ever. Many of these tools provide drag-and-drop functionality and other non-technical capabilities, so the average business user can build necessary dashboards. The maximum use of this software is seen among C suite users, teams in marketing, sales, and HR sectors.
Conclusion
Using the right data mining technique is sure to provide unprecedented insight into your wealth of data. With the leap in technology advancement happening around, data mining is and will continue to grow and find more in-depth insights.
Roll up your sleeves and take a deep dive to discover the unknown. Don’t just be amazed at the data in your server as you were with the candies in your hand. Find out what your data is showing you; you might be surprised by what you find in this Pandora.
To know more, contact us or email us @ info@orientalsolutions.com or call at +91 95000 47196
For the latest updates, follow us @ https://www.linkedin.com/company/oriental-solutions-private-limited/
