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• Classification: This technique uses predefined classes to assign
to objects. These classes describe the characteristics of items or Association Classification
represent what the data points have in common with each other. Rules
This data mining technique allows the underlying data to be more
neatly categorised and summarised across similar features or Predictive Clustering
Analysis
product lines.
Neural
• Clustering: This technique is similar to classification. However, Networks Decision
Trees
clustering identifies similarities between objects, then groups those K-Nearest
items based on what makes them different from other items. For Neighbour
(KNN)
example, while classification may result in product groups, such
as ‘shampoo’, ‘conditioner’, ‘soap’, and ‘toothpaste’, clustering may identify groups, such as ‘hair
care’ and ‘dental care’.
• Decision Trees: These are used to classify or predict an outcome based on a set list of criteria or
decisions. A decision tree is used to ask for input on a series of questions that sort the dataset
based on the responses given. Sometimes depicted as a tree-like visual, a decision tree allows for
specific direction and user input when drilling deeper into the data.
• K-Nearest Neighbour (KNN): This is an algorithm that classifies data based on its proximity to
other data. The basis for KNN is rooted in the assumption that data points that are close to each
other are more similar to each other than other bits of data. This non-parametric, supervised
technique is used to predict the features of a group based on individual data points.
• Neural Networks: This technique processes data through the use of nodes. These nodes comprise
inputs, weights, and an output. Data is mapped through supervised learning. This model can be
adjusted to produce threshold values to determine a model’s accuracy.
• Predictive Analysis: This technique strives to leverage historical information to build graphical or
mathematical models to forecast future outcomes. Overlapping with regression analysis, this data
mining technique aims at supporting an unknown figure in the future based on current data on hand.

Applications of Data Mining
In today’s age of information, it seems like almost every department, industry, sector, and company can
make use of data mining. Some common applications of Data Mining are:
Sales: The ultimate goal of a company is to make money, and data mining encourages smarter, more
efficient use of capital to drive revenue growth. Consider the point-of-sale register at your favourite local
coffee shop. For every sale, that coffeehouse collects the time a purchase was made, what products
were sold together, and what baked goods are most popular. Using this information, the shop can
strategically craft its product line.
Marketing: Once the coffeehouse above knows its ideal line-up, it can implement the changes.
However, to make its marketing efforts more effective, the store can use data mining to understand
where its clients see advertisements, what demographics to target, where to place digital ads, and
what marketing strategies most resonate with customers. This includes aligning marketing campaigns,
promotional offers, cross-sell offers, and programs to the findings of data mining.
Manufacturing: For companies that produce their own goods, data mining plays an integral part in
analysing how much each raw material costs, what materials are being used most efficiently, how much
time is spent along the manufacturing process, and what bottlenecks negatively impact the process.
Data mining helps ensure the flow of goods is uninterrupted and least costly.

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