I need 5 to 6 pages on topic “Brief on Data Mining Techniques, Methods, Algorithms and Tools”
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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.
Types of Data
Data mining can be performed on following types of data
Data Mining Implementation Process
Business understanding:
In this phase, business and data-mining goals are established.
Data understanding:
In this phase, sanity check on data is performed to check whether its appropriate for the data mining goals.
Data preparation:
In this phase, data is made production ready.
The data preparation process consumes about 90% of the time of the project.
The data from different sources should be selected, cleaned, transformed, formatted, anonymized, and constructed (if required).
Data cleaning is a process to "clean" the data by smoothing noisy data and filling in missing values.
For example, for a customer demographics profile, age data is missing. The data is incomplete and should be filled. In some cases, there could be data outliers. For instance, age has a value 300. Data could be inconsistent. For instance, name of the customer is different in different tables.
Data transformation operations change the data to make it useful in data mining. Following transformation can be applied
Data transformation:
Data transformation operations would contribute toward the success of the mining process.
Smoothing: It helps to remove noise from the data.
Aggregation: Summary or aggregation operations are applied to the data. I.e., the weekly sales data is aggregated to calculate the monthly and yearly total.
Generalization: In this step, Low-level data is replaced by higher-level concepts with the help of concept hierarchies. For example, the city is replaced by the county.
Normalization: Normalization performed when the attribute data are scaled up o scaled down. Example: Data should fall in the range -2.0 to 2.0 post-normalization.
Attribute construction: these attributes are constructed and included the given set of attributes helpful for data mining.
The result of this process is a final data set that can be used in modeling.
Modelling
In this phase, mathematical models are used to determine data patterns.
Evaluation:
In this phase, patterns identified are evaluated against the business objectives.
Deployment:
In the deployment phase, you ship your data mining discoveries to everyday business operations.
Data Mining Techniques
1.Classification:
This analysis is used to retrieve important and relevant information about data, and metadata. This data mining method helps to classify data in different classes.
2. Clustering:
Clustering analysis is a data mining technique to identify data that are like each other. This process helps to understand the differences and similarities between the data.
3. Regression:
Regression analysis is the data mining method of identifying and analyzing the relationship between variables. It is used to identify the likelihood of a specific variable, given the presence of other variables.
4. Association Rules:
This data mining technique helps to find the association between two or more Items. It discovers a hidden pattern in the data set.
5. Outer detection:
This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. Outer detection is also called Outlier Analysis or Outlier mining.
6. Sequential Patterns:
This data mining technique helps to discover or identify similar patterns or trends in transaction data for certain period.
7. Prediction:
Prediction has used a combination of the other data mining techniques like trends, sequential patterns, clustering, classification, etc. It analyzes past events or instances in a right sequence for predicting a future event.
Challenges of Implementation of Data mine:
Data mining Examples:
Example 1:
Consider a marketing head of telecom service provides who wants to increase revenues of long distance services. For high ROI on his sales and marketing efforts customer profiling is important. He has a vast data pool of customer information like age, gender, income, credit history, etc. But its impossible to determine characteristics of people who prefer long distance calls with manual analysis. Using data mining techniques, he may uncover patterns between high long distance call users and their characteristics.
For example, he might learn that his best customers are married females between the age of 45 and 54 who make more than $80,000 per year. Marketing efforts can be targeted to such demographic.
Example 2:
A bank wants to search new ways to increase revenues from its credit card operations. They want to check whether usage would double if fees were halved.
Bank has multiple years of record on average credit card balances, payment amounts, credit limit usage, and other key parameters. They create a model to check the impact of the proposed new business policy. The data results show that cutting fees in half for a targetted customer base could increase revenues by $10 million.
Data Mining Tools
Following are 2 popular Data Mining Tools widely used in Industry
R-language:
R language is an open source tool for statistical computing and graphics. R has a wide variety of statistical, classical statistical tests, time-series analysis, classification and graphical techniques. It offers effective data handing and storage facility.
Learn more here
Oracle Data Mining:
Oracle Data Mining popularly knowns as ODM is a module of the Oracle Advanced Analytics Database. This Data mining tool allows data analysts to generate detailed insights and makes predictions. It helps predict customer behavior, develops customer profiles, identifies cross-selling opportunities.
Learn more here
Benefits of Data Mining:
Disadvantages of Data Mining
Data Mining Applications
Applications | Usage |
Communications | Data mining techniques are used in communication sector to predict customer behavior to offer highly targetted and relevant campaigns. |
Insurance | Data mining helps insurance companies to price their products profitable and promote new offers to their new or existing customers. |
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. |
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. |
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. |
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. |
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. |
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 eCommerce store. |
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. |
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. |
Bioinformatics | Data Mining helps to mine biological data from massive datasets gathered in biology and medicine. |
Kindly revert for any queries
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