What is Data Mining - Database Design - Lecture Slides, Slides for DBMS (Database Management Systems). English and Foreign Languages University

DBMS (Database Management Systems)

Description: This lecture slide is very easy to understand and very helpful to built a concept about the foundation of computers and Database Design.The key points in these slides are:What Is Data Mining, Principle of Sorting, Knowledge Discovery from Data, Pattern Analysis, Data Archeology, Data Dredging, Information Harvesting, Business Intelligence, Nonoperational Data, Data Warehouses, Data Cleaning
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Data Mining

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What Is Data Mining? Data mining is the principle of sorting through large amounts of data and

picking out relevant information. In other words… • Data mining (knowledge discovery from data)

– Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data

• Other names – Knowledge discovery (mining) in databases (KDD), knowledge

extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

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Some Definitions • Data : Data are any facts, numbers, or text that can be

processed by a computer. – operational or transactional data such as, sales, cost, inventory,

payroll, and accounting – nonoperational data, such as industry sales, forecast data, and

macro economic data – meta data - data about the data itself, such as logical database

design or data dictionary definitions

• Information: The patterns, associations, or relationships among all this data can provide information.

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Definitions Continued.. • Knowledge: Information can be converted into knowledge

about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in terms of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.

• Data Warehouses: Data warehousing is defined as a process

of centralized data management and retrieval.

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Data Warehouse example

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Data Rich, Information Poor

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Data Mining process

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Knowledge discovery from data KDD process includes

• data cleaning (to remove noise and inconsistent data) • data integration (where multiple data sources may be combined) • data selection (where data relevant to the analysis task are retrieved from

the database) • data transformation (where data are transformed or consolidated into

forms appropriate for mining by performing summary or aggregation operations)

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KDD continued…. • data mining (an essential process where intelligent methods

are applied in order to extract data patterns.

• pattern evaluation (to identify the truly interesting patterns representing knowledge based on some interestingness measures)

• knowledge presentation (where visualization and knowledge

representation techniques are used to present the mined knowledge to the user)

Data mining is a core of knowledge discovery process

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Knowledge Discovery (KDD) Process

 Data mining—core of knowledge discovery process

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

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Data Mining: Confluence of Multiple Disciplines

Data Mining

Database Technology Statistics

Machine Learning

Pattern Recognition

Algorithm Other

Disciplines

Visualization

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Functionalities/Techniques:

• Concept/Class Description: Characterization and Discrimination

• Mining Frequent Patterns, Associations and correlations

• Classification and Prediction • Cluster Analysis • Outlier Analysis • Evolution Analysis

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Concept/Class Description: Characterization and Discrimination

• Data Characterization: A data mining system

should be able to produce a description summarizing the characteristics of customers.

• Example: The characteristics of customers who spend more than $1000 a year at (some store called ) AllElectronics. The result can be a general profile such as age, employment status or credit ratings.

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Characterization and Discrimination continued…

• Data Discrimination: It is a comparison of the general features of targeting class data objects with the general features of objects from one or a set of contrasting classes. User can specify target and contrasting classes.

• Example: The user may like to compare the general features of software products whose sales increased by 10% in the last year with those whose sales decreased by about 30% in the same duration.

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Mining Frequent Patterns, Associations and correlations

Frequent Patterns : as the name suggests patterns that occur frequently in data.

Association Analysis: from marketing perspective, determining which items are frequently purchased together within the same transaction.

Example: An example is mined from the (some store) AllElectronic transactional database.

buys (X, “Computers”)  buys (X, “software”) [Support = 1%, confidence = 50% ]

• X represents customer • confidence = 50% , if a customer buys a computer there is a 50% chance

that he/she will buy software as well. • Support = 1%, means that 1% of all the transactions under analysis

showed that computer and software were purchased together.

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Mining Frequent Patterns, Associations and correlations

• Another example: • Age (X, 20…29) ^ income (X, 20K-29K) 

buys(X, “CD Player”) [Support = 2%, confidence = 60% ]

• Customers between 20 to 29 years of age with an income $20000-$29000. There is 60% chance they will purchase CD Player and 2% of all the transactions under analysis showed that this age group customers with that range of income bought CD Player.

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Classification and Prediction

• Classification is the process of finding a model that describes and distinguishes data classes or concepts for the purpose of being able to use the model to predict the class of objects whose class label is unknown.

• Classification model can be represented in various forms such as

» IF-THEN Rules » A decision tree » Neural network

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Classification Model

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Cluster Analysis

• Clustering analyses data objects without consulting a known class label.

• Example: Cluster analysis can be performed on AllElectronics customer data in order to identify homogeneous subpopulations of customers. These clusters may represent individual target groups for marketing. The figure on next slide shows a 2-D plot of customers with respect to customer locations in a city.

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Cluster Analysis

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Outlier Analysis

• Outlier Analysis : A database may contain data objects that do not comply with the general behavior or model of the data. These data objects are outliers.

• Example: Use in finding Fraudulent usage of credit cards. Outlier Analysis may uncover Fraudulent usage of credit cards by detecting purchases of extremely large amounts for a given account number in comparison to regular charges incurred by the same account. Outlier values may also be detected with respect to the location and type of purchase or the purchase frequency.

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Evolution Analysis • Evolution Analysis: Data evolution analysis describes and

models regularities or trends for objects whose behavior changes over time.

• Example: Time-series data. If the stock market data (time- series) of the last several years available from the New York Stock exchange and one would like to invest in shares of high tech industrial companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing to one’s decision making regarding stock investments.

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