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WHAT IS KDD ?EXPLAIN ITS PROCESS?

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KDD :- Knowledge discovery in databases Knowledge Discovery in Databases also known as Data Mining , refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data stored in databases.

Steps Involved in KDD process:-

**Data cleaning** :- Data Cleaning is defined as removal of noisy and inconsistent data.

**Data integration** :- Data integration is defined as data from multiple data sources are combined into one common data source in the data integration process. This step can be avoided if collected from 1 source.

**Data selection** :- Data selection is the process where data relevant for data analysis task are retrieved from the database.

**Data transformation**:- The data is transferred from large volume to small volume using loss or lossless compression. If we avoid this step it will cause complex algorithm.

**Data mining** :- It is an essential process where intelligent methods are applied to extract data patterns potentially useful.

**Pattern evaluation**:- Check and keep only required pattern representing knowledge based on given measures.

**Knowledge Presentation**:- Knowledge presentation is known as technique which involves utilizing data visualization tools to represent the pattern which would be potentially useful.

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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 .Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD

The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

(1) Selection (2) Pre-processing (3) Transformation (4) Data Mining (5) Interpretation/Evaluation.

Creating a target data set: data selection

Data cleaning and preprocessing: (may take 60% of effort!)

Data reduction and transformation

Find useful features, dimensionality/variable reduction, invariant representation

Choosing functions of data mining

summarization, classification, regression, association, clustering

Choosing the mining algorithm(s)

Data mining: search for patterns of interest

Pattern evaluation and knowledge presentation

visualization, transformation, removing redundant patterns, etc.

Use of discovered knowledge

DBSCAN ALGORITHMDensity-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm Clustering based on density (local cluster criterion), such as density-connected points

Major features:- Handle noise - One scan - Discover clusters of arbitrary shape- Need density parameters as termination condition

Density-reachable:A point p is density-reachable from a point q w.r.t. Eps, MinPts if there is a chain of points p1, …, pn, p1 = q, pn = p such that pi+1 is directly density-reachable from pi

Density-connected A point p is density-connected to a point q w.r.t. Eps, MinPts if there is a point o such that both, p and q are density-reachable from o w.r.t. Eps and MinPts

ALGORITHMInput:D : a data set containing n objects, ε : the radius parameter, and MinPts: the neighborhood density threshold.Output:A set of density-based clusters.Method:1) mark all objects as unvisited;

2) do

3) randomly select an unvisited object p;

4) mark p as visited;

5) if the ε -neighborhood of p has at least MinPts objects

6) create a new cluster C, and add p to C;

7) let N be the set of objects in the ε -neighborhood of p;

8) for each point p' in N

9) if p' is unvisited

10) mark p' as visited;

11) if the -neighborhood of p' has at least MinPts points,add those points to N ;

12) if p' is not yet a member of any cluster, add p' to C;

13) end for

14)output C;

15) else mark p as noise;

16) until no object is unvisited;

AdvantagesDisadvantages1.DBSCAN is not entirely deterministic: border points that are reachable from more than one cluster can be part of either cluster, depending on the order the data is processed. 2.The quality of DBSCAN depends on the distance measure used in the function regionQuery(P,ε). The most common distance metric used is Euclidean distance. 3.DBSCAN cannot cluster data sets well with large differences in densities, since the minPts-ε combination cannot then be chosen appropriately for all clusters. 4.If the data and scale are not well understood, choosing a meaningful distance threshold ε can be difficult.0