What is Data Mining?
Data mining is an analytic process which is designed to inspect large amounts of data. It helps in searching systematic relationships and consistent patterns between variables. The final goal of big data mining is the prediction. In fact, predictive data mining is one of the most common types of data mining and has the most direct business applications. The process of big data mining basically comprises three stages:
- The initial exploration
- The model building or pattern identification with verification and validation
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Benefits of Data Mining and Analysis for Businesses
Fast and feasible decisions
Searching for information from a huge amount of data is time-consuming. But data mining and analysis helps to get the information much faster eliminating the possibility of making mistakes and incorrect decision making. It also helps in comparing the fetched information with various factors, so the decisions become more authentic and reliable.
Build powerful strategies
Data mining and provides reliable information which further helps in making different strategies. In other words, by analyzing information in different dimensions it becomes easier to make effective strategies and implement them. This can help companies to effectively expand their business boundaries and make authentic decisions.
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Gain a competitive advantage
Data mining and analysis help in comparing their own data with that of the competitors in different aspects. Competitive analysis is an important aspect of data mining that helps businesses gain a competitive advantage.
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What are the Different Data Mining and Analysis Techniques?
Classification analysis is a very important data mining and analysis technique that is used to retrieve relevant and important information about data, and metadata. It categorizes datasets in different classes. In classification analysis algorithms are applied to decide how new data should be classified. An excellent example of classification analysis would be our outlook email. In outlook, certain algorithms are used to characterize an email is legitimate or spam.
Association rule learning
The Association rule learning method in the data mining and analysis process helps in identifying interesting relations between different variables in large databases. This technique helps in uncovering hidden patterns in the data that can be further used to determine variables within the data and the concurrence of frequently appearing variables in the dataset. This is one of the data mining techniques that is used for examining and forecasting customer behavior and is highly recommended in a detailed retail industry analysis.
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Anomaly or outlier detection
This technique in the data mining and analysis process refers to the observation for data items in a dataset that does not match an expected behavior or an expected pattern. Anomalies are also known as novelties, outliers, noise, exceptions, and deviations. Often, they provide actionable and critical information. This is one of the data mining methods that can be used in a variety of domains, such as system health monitoring, intrusion detection, fault detection, detecting eco-system disturbances and event detection in sensor networks.
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Cluster analysis in the data mining and analysis process is actually a collection of data objects those objects are similar within the same cluster. That means the objects are similar to one another within the same group and are unrelated or dissimilar to the objects in other clusters. Clustering analysis is the method of data mining and analysis that helps in discovering clusters and groups in the data in such a way that the degree of association between two objects is highest if they belong to the same group. The result of this analysis helps in creating customer profiles.
Regression analysis is the data mining and analysis technique that helps to identify and analyze the relationship among different variables. It also aids in understanding the characteristic value of the dependent variable changes. This data mining method is generally used for prediction and forecasting.