Data Mining Versus Knowledge Discovery In Databases

 Data Mining Versus Knowledge Discovery In Databases

We need to separate data mining from KDD. In that point we discuss data mining versus knowledge discovery in databases process. Data mining is only a step in KDD. KDD means knowledge discovery in database. Data mining work as separate data from big data and that data will show with graphical pattern.

KDD-Knowledge Discovery Database-

KDD fallows fallowing step for extract data from big data file. In that steps data mining is one steps only.

KDD fallows fallowing steps respectively-

1. Data Selection

2. Pre-Processing

3. Transformation

4. Data Mining

5.Evaluation/Interpretation

1. Data Selection-

Data selection process is the first step in KDD, Extract data from big data is the aim of data mining and KDD. The whole data saved in centralized database-In that each and every data is stored about organization. In data selection firstly selecting data from centralized database for further process. In data selection gives only useful data from big database.

2. Pre-Processing-

In KDD data coming from lots of database files. In pre-processing work on incorrect or missing data. lots of data coming with unused so work on unused data and overfull data. Error full data is corrected or removed successfully in that pre-processing stage.

3. Transformation-

Data coming from pre-processing stage in that stage converted data in to a common format for next processing. common data format is important before data transferred to data mining stage.This stage is last stage for filter a data.

4. Data Mining-

Best step in KDD, in this step applies different algorithms on data from data mining and generate desired result. The perfect data created in that level, the data mining tool collect data from transformation stage and work on them and finally generate knowledge part for user. The whole result show to user with use of graphical representation and different diagrammatic format, that all formations are used for future decision. data mining process used their two tools for pattern creation and algorithm selection i.e. , predictive and descriptive.

5. Evaluation/Interpretation- 

How's result shows to user is decide here. In that stage various visualization (GUI) tools are used for display final result to user with use of pattern.

Explanation :

Knowledge Discovery in Databases (KDD) and data mining are closely related concepts in the field of data analysis, but they are not identical. KDD is a broader process that involves multiple stages to extract useful knowledge from large datasets, whereas data mining is one critical step within this overall process. Understanding the distinction between the two is important for effectively transforming raw data into meaningful insights.

Knowledge Discovery in Databases (KDD):

KDD refers to the complete process of identifying valid, novel, and actionable patterns from large volumes of data. It encompasses various stages including data selection, preprocessing, transformation, data mining, and interpretation. The KDD process begins with data selection, where relevant data from different sources is gathered. Next, data preprocessing removes noise, inconsistencies, and missing values to improve quality. During transformation, data is converted into appropriate formats for analysis. After that, the data mining stage applies algorithms to extract patterns or models. Finally, the evaluation and interpretation phase validates and presents the discovered knowledge in a useful format for decision-making. Thus, KDD is a comprehensive, iterative process focused on turning data into knowledge.

Data Mining:

Data mining, on the other hand, is a subset of KDD that specifically deals with the application of algorithms and statistical techniques to identify patterns, correlations, and trends in datasets. It uses methods such as classification, clustering, association rule mining, and regression to uncover hidden relationships. While KDD defines the “what” and “why” of knowledge discovery, data mining defines the “how.”

Key Differences:

• Scope: KDD is a complete process; data mining is one phase within it.

• Focus: KDD focuses on discovering and validating knowledge; data mining focuses on extracting patterns.

• Activities: KDD includes data cleaning and interpretation; data mining emphasizes algorithmic analysis.

• Outcome: KDD produces comprehensible knowledge; data mining produces raw analytical results.

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