What is Data Mining ?
Data
mining is a process of discriminating large data sets to identify patterns and
build relationships to solve problems through data analysis. Data mining tools
allow companies to predict future trends. Data mining for companies is also useful for converting
raw data into useful information. By using software to look for patterns in
large data sets, businesses can run easily, more effective marketing, increase
sales and reduce costs. Data mining depends on effective data collection,
warehousing, and computer processing. In data mining, association rules are created by
analyzing data for frequent if/then patterns, then using the support and
confidence criteria to locate the most important relationships within the data.
Support is how frequently the items appear in the database, while confidence is the number of times
if/then statements are accurate. Data mining techniques are used in many
research areas, including mathematics, cybernetics, genetics and marketing. While data mining
techniques are a means to drive efficiencies and predict customer behavior, if
used correctly, a business can set itself apart from its competition through
the use of predictive analysis.
Data mining is looking for hidden, valid, and
potentially useful patterns in huge data sets. Data Mining is all about
discovering unsuspected/previously unknown relationships amongst the data. It
is a multi-disciplinary skill that uses machine learning, statistics, AI and
database technology. The insights derived via Data Mining can be used for
marketing, fraud detection, and scientific discovery, etc. Data mining is also
called as Knowledge discovery, Knowledge extraction, data/pattern analysis,
information harvesting, etc.
How Data Mining Works ?
Data Mining involves exploring and analyzing
large blocks of information to glean meaningful patterns and trends. It can be
used in a variety of ways, such as database marketing, credit risk management,
fraud detection, spam Email filtering, or even to discern the sentiment or
opinion of users. The data mining process breaks down into six steps. First,
organizations collect data and load it into their data warehouses. Next, they
store and manage the data, either on in-house servers or the cloud. Business
analysts, management teams and information technology professionals access the
data and determine how they want to organize it. Then, applicationsoftware
sorts the data based on the user's results, and finally, the end user presents
the data in an easy-to-share format, such as a graph or table. The picture
below is a data mining step at work:
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| sumber : digitaltransformationpro.com |
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Because
we use data mining tools to sweep through the database. Also, to identify
previously hidden patterns in one step. There are examples of very good
patterns. Because this is a retail sales data analysis. That is to identify
unrelated products that are often bought together. Also, there are other
patterns of pattern discovery. That includes detecting fraudulent credit card
transactions.
Data Mining Techniques
in
this session of Data Mining Tutorial, we will explore the techniques used in
Data Mining:
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| sumber: Dataflair.com |
a.Artificial Neural
Networks, We use data mining in
non-linear predictive models. As this learn through training and resemble
biological neural network in
structure.
b.Decision Trees, As we use tree-shaped structures to
represent sets of decisions. Also, these rules are generated for the
classification of a dataset. These decisions generate rules for the
classification of a dataset. As there are specific decision tree methods that
include Classification
and Regression Trees and Chi-Square Automatic Interaction Detection (CHAID).
c.Genetic Algorithms,
There are the present genetic combination, mutation, and natural selection for
optimization techniques. That is design based on the concepts of evolution.
d.Nearest Neighbor
Method and Rules Induction, A technique that classifies
each record in a dataset based on a combination of the classes of the k
record(s) like. It in a historical dataset. Sometimes called the
k-nearest neighbour technique and the extraction of useful if-then rules
from data based on statistical significance.
Benefits of Data Mining
In Communications, data
mining techniques are used in communication sector to predict customer behavior
to offer highly targetted and relevant campaigns. In Insurance, data mining helps insurance companies to price their
products profitable and promote new offers to their new or existing customers. In
Education: Data mining benefits
educators to access student data, predict achievement levels and find students
or groups of students which need extra attention. For example, students who are
weak in maths subject. In manufacturing,
with the help of Data Mining Manufacturers can predict wear and tear of
production assets. They can anticipate maintenance which helps them reduce them
to minimize downtime. In Banking,
data mining helps finance sector to get a view of market risks and manage regulatory
compliance. It helps banks to identify probable defaulters to decide whether to
issue credit cards, loans, etc. In Retail,
Data Mining techniques help retail malls and grocery stores identify and
arrange most sellable items in the most attentive positions. It helps store
owners to comes up with the offer which encourages customers to increase their
spending. In Service Providers, Service
providers like mobile phone and utility industries use Data Mining to predict
the reasons when a customer leaves their company. They analyze billing details,
customer service interactions, complaints made to the company to assign each
customer a probability score and offers incentives. In E-Commerce, E-commerce websites use Data Mining to offer
cross-sells and up-sells through their websites. One of the most famous names
is Amazon, who use Data mining techniques to get more customers into their e-Commerce
store. In Super Markets, Data
Mining allows supermarket's develope rules to predict if their shoppers were
likely to be expecting. By evaluating their buying pattern, they could find
woman customers who are most likely pregnant. They can start targeting products
like baby powder, baby shop, diapers and so on. In Crime Investigation, Data Mining helps crime investigation
agencies to deploy police workforce (where is a crime most likely to happen and
when?), who to search at a border crossing etc. In Bioinformatics, Data Mining helps to mine biological data from
massive datasets gathered in biology and medicine.
Conclusion
Based on the information above, I can conclude
that Data Mining is all about
explaining the past and predicting the future for analysis.Data mining
helps to
extract information from huge sets of data. It is the procedure of
mining
knowledge from data.Data mining process includes business
understanding,Data Preparation,Modelling,Evolution,Deployment. Important
Data mining techniques are Classification, clustering, Regression,
Association
rules, Outer detection, Sequential Patterns, and prediction.R-language
and Oracle
Data mining are prominent data mining tools.Data mining technique helps
companies to get knowledge-based information.The main drawback of data
mining
is that many analytics software is difficult to operate and requires
advance
training to work on. Data mining is used in diverse industries such as
Communications, Insurance, Education, Manufacturing, Banking, Retail,
Service
providers, eCommerce, Supermarkets Bioinformatics
Dataflair
Team, February 2018, Data Mining Tutorials, (accesed via https://data-flair.training/blogs/data-mining-tutorial/, on 07/07/2019)
Rizky Abdilah
106218053
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