Making Sense of Data: Considering Top Data Mining Techniques

by September 18, 2020 0 comments

Data Mining

Deriving actionable insights from your data with essential data mining techniques.

Businesses today have access to massive amounts of data than ever before. These voluminous data are typically collected and stored in both structured and unstructured forms. These data are gleaned from various sources such as customer data, transactions, third-party vendors, and more. However, to make sense of the data is much challenging and requires relevant skills and tools and techniques to excerpt meaningful information from it. Data mining here has a role to play in extracting information from a given data set, identifying trends, patterns, and useful data.

Data mining refers to the usage of refined data analysis tools to discover previously unknown, valid patterns, and relationships in huge data sets. It integrates statistical models, machine learning techniques, and mathematical algorithms, such as neural networks, to derive insight. Thus, to make sense of your data, businesses must consider data mining techniques.

Here is a look at the top data mining techniques that can help extract optimal results.

 

Data Cleaning

As businesses often gather raw data, it requires to be analyzed and formatted accurately. By appropriate data cleaning, businesses can understand and prepare the data in different analytic methods. Typically, data cleaning and preparation involves distinct elements of data modeling, transformation, data migration, ETL, ELT, data integration, and aggregation.

 

Association

Association defines to identify a pattern in a transaction. It specifies that certain data, or events found in data, are related to other data or data-driven events. This technique is used to conduct market basket analysis, which is done to find out all those products that customers buy together regularly. It is useful in understanding customers’ shopping behaviors, providing businesses the opportunity to study sales data of the past and then predict future buying trends.

 

Clustering

Clustering is the process of finding groups and clusters in the data in such a way that the degree of association between two objects is highest if they belong to the same group and lowest otherwise. Unlike classification that puts objects into predefined classes, clustering for data puts objects in classes that are defined by it. Essentially, clustering mechanisms use graphics to define where the distribution of data is in relation to different sorts of metrics. This technique also uses different colors to show the distribution of data.

 

Classification

This data mining technique is generally used to classify different data in different classes. It is similar to clustering in a way as it also fragments data records into different segments. But unlike clustering, data analysts in classification analysis would know about different classes or clusters. They would even apply algorithms to determine how new data should be classified.

 

Outlier Detection

Simply finding patterns in data may not give a clear understanding that businesses want. Outlier analysis or outlier mining, which is the most crucial data mining technique, helps organizations determine anomalies in datasets. Outlier detection generally refers to the observation of data items in a dataset that do not match an expected pattern or expected behavior. Once businesses find deviations in their data, it becomes easier to understand the reason for anomalies and better prepare for any future occurrences to achieve business objectives.

 

Regression

This data mining technique refers to the process of detecting and analyzing the relationship between variables in a dataset. Regression analysis can help businesses understand the characteristic value of the dependent variable changes if any one of the independent variables is varied. It is primarily a form of planning and modeling and can be used to project certain costs, relying on other factors such as availability, consumer demand, and competition.

 

Sequential Patterns

It is particularly useful for data mining transactional data and focuses on divulging a series of events that take place in a sequence. It encompasses discovering interesting subsequences in a set of sequences, where the stake of a sequence can be measured in terms of various criteria like length, occurrence frequency, and so on. Once a company understands sequential patterns, it can recommend additional items to customers to spur sales.

 

Visualization

Data visualization is an effective technique for data mining. It grants users’ insight into data based on sensory perceptions that people can see. Also, data visualizations can be used through dashboards to unveil insights. Instead of simply using numerical outputs of statistical models, the enterprise can base dashboards on different metrics and use visualizations to highlight patterns in data visually.

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