Data mining is a buzzword that is often used to refer to any form of large-scale data or information processing, to extract useful knowledge for business use. The term is sort of misleading in some way when we are actually not mining for data, but mining for actionable knowledge from the data which is not apparent in its original form. The term is believed to have appeared the first time around 1990.
Data mining tasks are designed to be semi-automatic or fully automatic and on large sets of data to uncover patterns such as groups or clusters, unusual or over-the-top data called anomaly detection and dependencies such as association and sequential patterns. Once patterns are uncovered, they can be thought of as a summary of the input data and further analysis may be carried out using Machine Learning and Predictive analytics. For example, the data mining step might help identify multiple groups in the data that a decision support system can use. Note that data collection, data preparation, and reporting are not part of data mining.
There is a lot of confusion between data mining and data analysis. While data analysis is used to test statistical models that fit the dataset, for example, analysis of a marketing campaign, data mining uses Machine Learning and mathematical and statistical models to discover patterns hidden in the data.
In this article let us look at:
Formally speaking data mining is a process of searching for patterns in large data sets, that brings in methods from statistics, computer science, database management, and machine learning to derive knowledge that can be used to run a business more efficiently. Data mining has applications in any industry that has a good deal of data generated and stored. Data mining is broadly categorized as
In this type of data mining, certain routines are run to obtain summary statistics of the data set like, count, average, etc. It gives a fair bit of idea about the data at hand.
With previously available data or historical data, data mining can be used to make predictions about critical business metrics based on data’s linearity. For example, predicting the volume of business next quarter based on performance in the previous quarters over several years.
There are a number of data mining functionalities that the organized and scientific methods offer. Let us look at a few major ones.
Classification is a data mining technique that categorizes items in a collection, based on some predefined properties. A training set containing items whose properties are known is used to train the system to predict the category of items from an unknown collection of items. It uses methods like if-then, decision trees or neural networks to predict a class or essentially classify a collection of items.
Also known as Market Basket Analysis for its wide use in retail sales, Association analysis aims to discover associations between items occurring together frequently. Association analysis is based on rules having 2 parts:
An antecedent is an item in a collection, which, when found, also indicates a certain chance of finding a consequent in the collection. In other words, they are associated. From the data association inferences like,
If a customer buys potato chips, he is about 60% like to buy soft drinks along with it. So, over a big chunk of data, you see this association with a certain degree of confidence.
Cluster Analysis, is fundamentally similar to classification, where similar data are grouped together with the difference being that a class label is not known. Clustering algorithms group data based on similar features and dissimilarities. Used in image processing, pattern recognition and bioinformatics, clustering is a popular functionality of data mining.
A class or concept implies there is a data set or set of features that define the class or a concept. A class can be a category of items on a shop floor, and concept could be the abstract idea on which data may be categorized like products to be put on clearance sale and non-sale products. There are two concepts here, one that helps with grouping and the other that helps in differentiating.
Characterization involves summarization of general features of the data, resulting in specific rules that define a target class. A data analysis technique called Attribute-oriented Induction is employed on the data set for achieving characterization.
Discrimination is used to separate distinct sets of data, based on the disparity in attribute values. It is a comparison of features of a class with features of one or more contrasting classes.
In data mining, there are primarily two types of predictions, numeric predictions and class predictions. Numeric predictions are made by creating a linear regression model that is based on historical data. Prediction of numeric values helps businesses ramp up for a future event that might impact business in a positive or a negative way. Class predictions are uses to fill in missing class information for products using a training data set where the class for products is known.
Outlier analysis is important to understand the quality of data. If there are too many outliers, you cannot trust the data or draw patterns out of it. An outlier analysis of the data that cannot be grouped into any classes by the algorithms is pulled up. An outlier analysis tries to determine if there is something out of turn in the data and does it indicate a situation that a business needs to take account of and take measures to mitigate it. In most cases, outliers are data abnormalities. Still, it is important to take note of each so unusual activities or events that trigger a business impact can be detected well in advance.
Evolution Analysis pertains to study of data sets that undergo change over a time period. Evolution analysis models are designed to capture evolutionary trends in data helping in characterizing, classifying, clustering or discrimination of time-related data.
One of the functions of data mining is finding data patterns. Frequent patterns are things that are discovered to be most common in data. Various types of frequency can be found in the dataset.
Correlation is a mathematical technique for determining whether and how strongly two attributes are related to one another. It determines how well two numerically measured continuous variables are linked (e.g. height and weight). Researchers can use this type of analysis to see if there are any possible correlations between variables in their study for example, people with good height tend to be heavier. It refers to the various types of data structures, such as trees and graphs, that can be combined with an item set or subsequence.
The adoption of data mining functionalities and techniques has increased rapidly in the last couple of decades due to the evolution of data warehousing technology and the growth of big data, helping companies transform their raw data into useful knowledge.
Data Mining is used heavily in retail and e-commerce to understand purchase patterns, shifting trends over time and studying and classifying customer purchase patterns. A good data mining team will ensure your business will adapt to change quickly and keep you on top of events that impact business negatively.
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