For long, data scientists have been trying to find patterns and trends in data. To do so, they need to mine through a substantial amount of data. The more the data, the more accurate the insights and information. The recent explosion of big data has posed new problems to data scientists who are struggling to process such data sets. The data sets go way beyond the current storage capacity and computing power. So, it is essential to make the data mining techniques more efficient to gain relevant insights within seconds. These data mining techniques extend beyond just simple statistical analysis by analyzing millions and billions of data points to present in-depth insights. So what are the top data mining techniques used by companies to make sense out of their data?
Association Rule Learning
More like how humans develop knowledge, association rule implies the same principle of learning. A kid learns that fire is hot, and anything with flame will be hot as well. Similarly, interesting relationships can be uncovered between different variables in large datasets by using association rule learning. It is also the most straightforward data mining technique. The data scientist makes a simple correlation between two or more items to identify the same type of patterns. For instance, in a retail setting, a retailer may discover that a certain customer always buys eggs when they buy milk, and therefore they may suggest eggs the next time they put milk in their cart. Additionally, the technique can be used to determine product clustering, catalog design, shopping basket data analysis, and store layout.
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One of the easiest ways to teach a machine is by classifying data into close groups. The data scientists assign the given data into a pre-defined category, and the machine can learn to accurately predict in the future what data belongs to which category. One of the most efficient forms of this technique can be seen in Gmail, where the sorting algorithm can automatically identify if a mail is spam, promotional, update, or personal. This data mining techniques can also be used across other industries to classify customers based on age and social group.
Clustering is more or less similar to classification analysis, with the only difference being that there is no pre-determined category. Cluster here refers to a collection of data objects that are close together when plotted on a graph. It indicates that two objects that are closer to each other exhibit more or less the same properties than those that are far away. It helps data scientists to identify customer profiles from scratch. However, it can be challenging to pinpoint a specific cluster, since one data set can be the same distance apart from two different clusters.
Anomaly or Outlier Identification
In statistical terms, data is dispersed consistently, with the majority of the data point clustering around the average. However, there can be few outliers that can be on the extreme end of the spectrum. Sometimes it can occur naturally, but usually, it presents the analyst with a concerning observation. Such data mining technique is used in fraud detection, intrusion detection, and system health monitoring. For instance, a customer who spends on average $50 per transaction, suddenly spends $10,000 in a single transaction can signal that a fraud has occurred.
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Decision trees are closely related to other data mining techniques. It can be used as a part of selection criteria or to support the use of specific data within the overall structure. Each decision tree starts out with a simple question and based on the answers, further questions are asked which helps to be sorted to a particular category. With time, accumulating many answers can enable data scientists to make a prediction based on each type of answer. For instance, such data mining techniques can start out with a simple question like whether a customer is a male or a female; then based on the answer, further questions could be asked. Based on the answers obtained, accurate predictions can be made.