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• Classification: This technique uses predefined classes to assign
to objects. This data mining technique allows the underlying Association Classification
data to be more neatly categorised and summarised across Rules
similar features or product lines.
• Clustering: This technique is similar to classification. However, Predictive Clustering
Analysis
clustering identifies similarities between objects, then groups
Neural
those items based on what makes them different from other Networks Decision
Trees
items. Neighbour
K-Nearest
• Decision Trees: These are used to classify or predict an outcome (KNN)
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.

• 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.
• 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.
• Predictive Analysis: This technique strives to leverage historical information to build graphical
or mathematical models to forecast future outcomes.

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:
Marketing: Once a company knows its ideal line-up, it can implement the changes. 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.
Fraud Detection: The heart of data mining is finding patterns, trends, and correlations that link data
points together. Therefore, a company can use data mining to identify outliers or correlations that
should not exist.
Human Resources: Data mining can correlate data on retention, promotions, salary ranges, company
benefits, and employee satisfaction surveys to get a better understanding of why employees leave
and what entices recruits to join.
Customer Service: Customer satisfaction may be caused (or destroyed) for a variety of reasons. Data
mining gathers operational information about customer interactions and summarises findings to
determine weak points as well as highlights of what the company is doing right.

NEURAL NETWORKS
A neural network is a network of biological neurons, in case of humans, or, in a modern sense, an
artificial neural network, composed of artificial neurons or nodes. Artificial Neural Networks (ANNs)
are used for solving artificial intelligence (AI) problems. The connections of the biological neurons
are modelled in artificial neural networks as weights between nodes. A positive weight reflects an
excited connection, while a negative weight reflects an inhibitory connection. Finally, an activation

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