#1 Introduction To Data Mining, Types Of Data |DM|

Introduction to Data Mining: Importance of Data Warehousing and Data Mining, Kinds of Patterns, Technologies, Applications, Major Issues in Data Mining, Data Objects and Attributes Types, Statistical Descriptions of Data, Estimating Data Similarity and Dissimilarity
Data exploration and pre-processing: Data Visualization, Quality data, Data Cleaning, Data Integration, Data Reduction, Data Transformation, Discretization and Concept Hierarchy Generation.
Data Warehouse and OLAP Technology: Basic Concepts of Data warehouse, Data Modeling using Cubes and OLAP, DWH Design and usage, Implementation using Data Cubes and OLAPs, Data Generalization with AOI.
Data Cube Technology: Preliminary Concepts of Data Cube Computation, Data Cube Computation Methods: Multi-way Array Aggregation for Full Cube, BUC, Star-cubing, Pre-computing shell fragments for High dimensional OLAP
Mining Frequent Patterns Based on Associations and Correlations: Basic Concepts, Frequent Item set Mining Methods: Apriori Algorithm, Association Rule Generation, Improvements to A Priori, FP- Growth Approach, Pattern Evaluation Methods
Classification &Prediction: Basic Concepts, Decision Tree Induction, Bayes Classification, Rule- Based Classification, Model Evaluation and Selection, Techniques to Improve Classification Accuracy, Classification by Back Propagation, K-nearest neighbor classifier.
Cluster Analysis: Basic Concepts and issues in clustering, Types of Data in Cluster Analysis, Partitioning Methods, Hierarchical Methods, DBSCAN, Grid Based Methods, Evaluation of Clustering Solutions


This Andhra University college syllabus can we follow this playlist

thankyou so much mam for your valuable information😍😙😘😘

Mam please give Pdf of data mining

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Madam can we get notes in pdf manner

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Mam please send me notes

ENG Radhhaaa🤠

Even now l am having the subject of data warehouse and mining

Mam pls do videos on data analytics using R

Mam pls do videos on data analytics using R

Mam plz explain unit 5 in data mining

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Mam can we get these material

Thank You for uploading mining concepts...explanation is very clear...i think you are from andhra pradesh....thank you

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(ps: he told me to comment it here)

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Hi Madam. Can you clarify this thing? is this course based on Data Mining. Concepts and Techniques, 3rd Edition? Please help..

I guess "Querying" can only be done with the help of "Select" statements. Please refer to Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), Transaction Control Language (TCL), and Transaction Querying Language (TQL). SELECT falls under TQL.

INSERT, DELETE, and CREATE do not fall under Querying. CREATE falls under DDL and INSERT & DELETE fall under DML.

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Your explanation is really great. I can understand alot just from watching your videos. You went really deep while explaining and that is really nice comparing to our teachers explanation😊

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data mining is extraction of useful information and pattern from set of data, eg:- like managing a general store in a format so customer get products easily and frequent buying product are in front of the store with co related product like milk with bread.

Please provide note pdf link

DWDM pdf share cheyandi

U r speaking very quickly

mam, can you share the notes for DWDM Datawarehouse and Data Mining Combined

1- Introduction to Data Mining, Types of Data

-Data Mining (DM) - the procedure of extracting and analyzing very large amounts of data sets in order to generate new information or knowlege

-Types of data that may be mined

-Databases - Relational Database Management System - RDBMS - row (tuple) column (attribute). While mining, trends or data patterns may be determined, based on previous data, predicting situations, analysing deviations

-Datawarehouses - collecting data from different sources, integrating data and collecting more data. Querying (CRUD) and decision making based upon data. Data is stored in a multi-dimensional structure (datacube) where each dimension is each attribute, eg; time, location, item type

-Transactional data - each record, attribute, is a transaction; a sale, a flight, a web-page click

-Other data - Sequence data (stockmarket) data streams (continuous) spatial data (maps) engineering & design (circuits) hypertext, multimedia, web....
.

Thankyouuu ❤

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upload foundation of machine learning videos

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Can i get notes ?

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notes available kra dijiye please didi on the discription

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Mam plz provide your hand written notes in description. It will help to students to save time.

Mam, Is this play list will help me to get DATA MINING certificate from NPTEL???