Businesses have all the sources and methodologies to collect and store voluminous data in structured and unstructured forms. These data are gleaned from multiple sources such as website, customer data, social media, transactions, 3rd-party vendors, and more. So, businesses of any form and size today possess humongous data with them.
Having said this, it is necessary to understand that these data creates noise as the information is buried deep inside. It is significant to figure out this information for taking significant business choices. It is important to mine this information, extricate data, recognize patterns, examples, and make the information helpful for business purposes. As a result, businesses are looking for more aspiring data science professionals to make sense of their voluminous data.
Let’s understand data mining, its importance, and the major techniques involved here.
Data mining is nothing but a set of methodologies applied to large and complex databases to eliminate the randomness and discover the hidden pattern. Data mining methods are computationally intensive and use refined data analysis tools. While doing so, it integrates statistical models, mathematical algorithms, and machine learning techniques.
Data mining is very much essential for businesses, as it helps in many ways. Explore some of the benefits a business can have from data mining.
Benefits of data mining
Data mining is a cost-effective and efficient solution. It helps to:
- Get knowledge-based information
- Make changes in production and operation
- Make informed decisions
- Automate prediction of trends
- Automate discovery of hidden patterns
- Implement in both new and existing systems
- Accelerate data analysis
- Detect frauds quickly
- Increase company revenue
- Increase website optimization
- Increase brand loyalty
Popular data mining techniques
With these benefits, let’s deep dive into the most popular data mining techniques used for making the most out of the data.
Classification analysis technique
With classification analysis technique, you can retrieve important and relevant information about data and metadata. The professionals who have taken a data science career as analysts classify data into different classes by applying algorithms.
Data mining frameworks can get classified as per the type of data handled (E.g. Multimedia, text data), database involved (relational, transactional), discovered knowledge (discrimination, clustering), or as per data analysis approach (neural networks, genetic algorithms).
Association rule learning technique
Association rule is recommended in the retail industry for basket data analysis, determine shopping, etc. It is useful to examine and forecast behavior, build programs for machine learning, and, etc. It works on the if/then statements to identify the relationship between the objects.
The various algorithms include:
- Apriori algorithm -for market basket analysis
- Eclat algorithm – for itemset mining
- Frequent pattern growth classification – for databases
Outlier detection technique
It is the primary step in data-mining applications. It is helpful to distinguish univariate vs multivariate and multivariate vs nonparametric procedures. It is based on clustering, distance measures, and spatial methods.
They are categorized as:
- Statistical distribution-based approach – for determining minimal clinically important changes
- Distance-based approach- analyze inventory policies in manufacturing
- Density-based approach- discover arbitrary data and handle data noise
- Deviation-based approach –monitor process variability
Clustering Analysis Technique
They are used to identify groups of similar objects from multi-variate data sets. The data sets may be from geospatial or marketing. A few of the clustering methods include:
- Centroid clustering – segment customer list
- Density clustering – group closely related data points
- Distribution clustering –identify the probability of a point that belongs to a cluster
- Connectivity clustering -recognize data point as its cluster
Regression Analysis Technique
It is a kind of predictive modeling technique that investigates the relationship between target and predictor. It is mainly used for time series modeling, forecasting, and determining the causal effect relationship in variables.
It is used to project the data types. It recognizes the historical trends and chartsfor an accurate prediction of the future. For instance, you can review customers’ credit histories and purchases done. It helps to predict the credit risk of select customers in the future.
Sequential Patterns Technique
The technique discovers similar patterns and helps businesses to identify a set of items. Businesses can use this data and recommend customers for purchase, and make better deals depending on their purchasing frequency.
It helps to recognize patterns in transaction data over time.
Decision Trees Technique
This technique plays an important role in data mining. They are used to handle non-linear data sets. It can be used in real life situations like civil planning, law, business, and engineering.
They are used to assess prospective growth opportunities, find prospective clients, and serve as a support tool in many fields.
So, these are some of the data mining techniques.
To conclude, data mining is a multi-disciplinary skill. It uses machine learning, statistics, Artificial Intelligence, and database technology. Earning the best data scientist certification enables you to excel in data mining. Use data mining for marketing, fraud detection, scientific discovery, and other important inputs as per your industry vertical.
Discover the knowledge, extract knowledge, analyze patterns, and harvest information efficiently for your organization. Get certified today.