One relatively popular technology that is gaining momentum in the world of data analysis is Data Mining. Though it’s a new term for many people but the technology is nothing new. Well, it existed even a few years back the industry is starting to make use of this technology with the advent of ‘Big Data’.
Data Mining can be described as an analytics process that helps in exploring a large amount of data that is considered to be relevant to the market or business. In a layman’s term, it can be defined as the process of discovering valuable data by analyzing the huge amount of data with the help of many data mining techniques. However, the ultimate goal of data mining is the prediction.
Data mining is often referred to as Knowledge Discovery Databases (KDD) and many industries are making use of this technology in order to improve their business efficiency. The term data mining first appeared in the year 1990s and before that it was known by ‘Data Fishing’.
Now, let’s examine various classes of data mining process which are often used in various data mining projects by the data mining companies.
Anomaly or Outer Detection
It can be defined as the process of searching for data items within a dataset which don’t match with a proposed pattern or expected behavior. Anomalies are often touted to as outliers or exceptions and most of the time it provide crucial information. However, it remains numerically distant from the rest of the data. Thereby, it points out that the extraordinary things need to have additional analysis. Well, an outlier can help in indicating the bad data, as well as provide data that can be very interesting.
It can be defined as the process of which interesting relations between different variables in large databases can be discovered. Association can help in unearthing the hidden patterns within the data. It can be used for identifying the variables with the data and the data that occurs more frequently.
It involves identifying data groups which are similar to each other. It helps in understanding the similarities and the differences within the data set. The common features can be used in improving the targeting algorithms. For example, it can help in targeting potential customers with same buying behavior.
The result obtained from clustering analysis can help in the creation of personas. It can help in defining the various user types within a targeted demographic.
With this technique, pertinent information about data and meta data can be easily obtained. Well, it can help in identifying the category to which the data belongs. However, it can be closely linked to cluster analysis as the data obtained can be used for cluster data.
This technique can help in explaining the dependency between the variables. This technique can be used for determining the various level of customer satisfaction and ways in which customer loyalty can get affected.
Data mining techniques can help businesses to add more value to their business. It can be stated that the basic aim of data mining is to obtain useful information that can be easily understood and used by business organizations for making their business even better.