Data mining techniques
This post introduces the concept of data mining and describes some widely accepted techniques.
It's no misnomer that data volume is growing and is vital for any organization. Data mining is at the crux of all the decision-making that happens within organizations these days.
What is data mining?
Simply put, data mining extracts meaningful and useful information from a large set of raw data.
The data and databases come from various data sources, and you can store them in different data warehouses. You might assume that data mining refers to the extraction of raw data, but that's incorrect. Data mining involves sifting through your collected data to find meaningful patterns and trends. You can then use your analysis to detect fraud, market data or services, manage credit risk, filter unwanted mail or messages, or explore consumer opinion.
Sequential patterns: This technique identifies patterns in your data sets that occur at regular intervals. For example, the sale of specific products might spike just before the holidays or more people visit your portal on the weekend.
Classification: You can use this technique to retrieve relevant information about data and metadata and categorize data in different classes. You can also draw further conclusions about classified data. For instance, if you're evaluating data on individual customers' financial background and purchase history, you can determine their credit risk. Based on that information, you can learn even more about the behavior of these customers.
Association: This data mining technique helps to find the association between two or more data items. You can identify specific events or attributes that show a high correlation with another event or attribute. Association enables you to discover a hidden pattern in the data set. For instance, you might notice that when your customer purchases a specific item, they also often buy a related product. This information helps to populate the People Also Bought section of online stores.
Outlier detection: Outer detection, also called outlier analysis or outlier mining, involves observing data items in the data set that do not match the expected pattern or behavior. You can use this technique to identify intrusion and fraud. In banking systems, data outliers, such as a sudden spike in online spending, transactions from foreign locations, multiple transitions within minutes, raise a red flag.
Clustering: Clustering, while similar to classification, involves grouping chunks of data together based on their similarities. This process highlights the differences and similarities between the data. For instance, you might choose to treat different clusters of your audience based on demographics such as their income or viewing habits.
Regression: Regression identifies and analyzes the relationship between variables. You can use it to determine the likelihood of a specific variable occurring based on the presence of other variables. For example, you could project price based on availability, consumer demand, and competition. Regression helps you to uncover the exact relationship between variables in each data set.
Prediction: You can use prediction, one of the most valuable data mining techniques, to project the types of data you'll see in the future. Prediction combines data mining techniques like trends, sequential patterns, clustering, classification, and others. It analyzes past events or instances in sequence to predict a future event. For example, you might review consumers' previous purchases to predict the maximum amount they'll spend on a product. It can also help you to identify target customers for a new launch.
Data mining helps organizations make precise and correct decisions. It helps you run successful campaigns, make predictions, and analyze customer behavior. You can expect effective results by using any of the techniques described in this post.
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