5 Most Commonly Used Data Mining Techniques and Their Benefits

Data is everywhere; and especially in research works, data occupies a quintessential position. Gathering data is the first step in a scientific inspection. But scientific investigation is not just limited to compilation of data points. The researchers should decide ‘which’ data is relevant to their study and ‘what’ to do with the collected data. This is when data mining comes into the picture!

Data Mining techniques are proven to be useful for researchers for analysing the extracted data. However, it should be noted that PhD research work involving statistics, is very arduous to accomplish. This is owed (in large) to the inability of first-timers to incorporate appropriate graphs, tables, charts, etc in their research document. To help scholars achieve their PhD, writers out there are offering plagiarism free PhD thesis writing help across the globe.

In the recent years, several data mining techniques have been developed and are being used in various domains. These include clustering, association, classification, prediction, decision tree and sequential pattern techniques. Read the below section to acquire adequate knowledge of data mining techniques.

  1. Clustering technique –  This type of technique has characteristics similar to that of automatic technique. Clustering method identifies the group of individuals, classes and assigns classes for predefined classes. Depending on the characteristics of data, we have different types of clustering data mining technique; centroid-based, distributed-based, connectivity-based and density-based. Some of the reasons why clustering technique is important in data mining are scalability, high dimensionality, and interpretability.
  2. Association technique – This technique also known as relation technique is one most popular approach among the data analysts. In association technique, a pattern will be discovered based on relationship between the items in the same transaction. The main advantage of this technique is that association can exist between any attributes.
  3. Prediction technique – This technique not just determines the relationship between the independent variables but also determines the relationship between the dependent and independent variables. This method is considered to be a valuable method as it is useful in predicting the future events.
  4. Decision tree technique – Decision tree technique is termed as the easiest data mining model. A decision tree is basically a predictive model that can be viewed as a tree. This technique is mostly used for the segmentation of the original data sets. The major benefits of using this technique are it does not require any domain knowledge, easy to comprehend, and is simple & fast. Although this technique is the easiest among the others, the graphs pertaining to decision tree are quite complex. Hence it is wise to take help from experienced writers delivering PhD thesis writing assistance as they know how to plot the graph and include it in the research document.
  5. Sequential pattern technique – Sequential pattern technique identifies similar patterns and trends in data transaction. This technique consists of discovering useful and unexpected patterns in the databases. Using sequential pattern technique, one can discover patterns such as subgraphs, associations, frequent itemsets and periodic patterns.

Data mining is an important aspect in the research journey. It helps the researcher identify the insights of the data and in converting data into meaningful information. Depending on your requirements choose the suitable data mining technique, obtain the relevant information and complete your thesis like a pro.

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