data mining and knowledge

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Presented by :1.Kartik N. Kalpande.

The data mining is the process in Data Knowledge

Discovery in Database that produces useful patterns or the modules from the data, Database.

Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.

The KDD stands for Knowledge Discovery in Database.

It refers to the overall process of discovering useful knowledge from the data. 2

Data Mining and KDD

Classification:-

Mining patterns that can classify future data.

Association Rule Mining:-Mining any rule of the form X Y,

where X and Y are sets of data items.

Clustering:-Identifying a set of similarity groups in

the data.3

Main Data Mining Tasks

Sequential Pattern Mining:A sequential rule: A B, says that event A will

be immediately followed by event B with a certain confidence.

Deviation detection: Discovering the most significant changes in

Data.

Data visualization: - Using graphical or Diagrammatically methods to show patterns in data. 4

Main data mining tasks (cont. …)

Valid: generalize to the future. Novel: what we don't know. Useful: be able to take some action. Understandable: leading to insight. Iterative: takes multiple passes. Interactive: human in the loop .

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What is data mining?

Data Mining process :-

OriginalData

TargetData

PreprocessedData

TransformedData

Patterns

KnowledgeSelection

PreprocessingTransformation

Data Mining

Interpretation

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Regression:- Assign a new data record to one of several predefined categories or classes. Regression deals with predicting real-valued fields. Also called supervised learning. Clustering: Partition the dataset into subsets or groups such that elements of a group share a common set of properties.

Data Mining Techniques

1. Selection: Selecting data relevant to the analysis task from

the database 2. Preprocessing: Removing noise and inconsistent data;

combining multiple data sources 3. Transformation: Transforming data into appropriate forms

to perform data mining 4. Data mining: Choosing a data mining algorithm which is

appropriate to pattern in the data; Extracting data patterns 5. Interpretation/Evaluation : Interpreting the patterns into

knowledge. 8

KDD Methods..

Related Areas of Data Mining And KDD

Database technologyand data warehouses efficient storage,

access and manipulationof data

DM

statistics

machinelearning

visualization

text and Web mining

softcomputing pattern

recognition

databases

Conti…

Statistics, machine learning, pattern recognition and soft computing:-

Techniques forclassification and knowledge extractionfrom data.

DM

Statistics

MachineLearning

Visualization

Text and web mining

SoftComputing Pattern

Recognition

Databases

Conti…Text And Web

Mining:- Web page analysis, Text categorization, filtering and structuring of textual information Natural language

processing

DM

Statistics

MachineLearning

Visualization

Text and web mining

SoftComputing Pattern

Recognition

Databases

Text and web mining

Knowledge discovery can be broadly defined

as the automated discovery of novel and useful information from commercial databases. Data mining is one step at the core of the knowledge discovery process, dealing with the extraction of patterns and relationships from large amounts of data. Data Mining Techniques are used to analyze data and extract useful information from large amount of data.

Conclusion..

Thank You…..!!!

Any Queries …??

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