DATA MINING IPT

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DLK Career Development Center offers a way for the students to work with live application by offering a inplant training program. We will be encouraging the students to work with Real time projects.

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DATA MINING IPT TRAINING

DLK Career Development Centre in Chennai is your premier destination for specialized Data Mining IPT in chennai. Our expert trainers offer comprehensive and hands-on training to equip you with the essential skills needed in today’s data-driven world. Whether you’re a student or a professional looking to enhance your knowledge, our Data Mining IPT training in Chennai provides the perfect opportunity to learn and grow in the field of data analysis and mining.

Our Data Mining IPT training in Chennai focuses on practical applications and real-world scenarios, ensuring that you are well-prepared to handle the challenges of data analysis projects. By enrolling in our course, you’ll gain valuable insights into data mining techniques, tools, and methodologies, setting you on a path to a successful and rewarding career in the data industry. Don’t miss this opportunity to acquire the skills that are in high demand in today’s job market. Join DLK Chennai for Data Mining IPT training and take the first step towards a data-driven future.

DLK Career Development Center holds out top fine ipt in Chennai with an exceedingly skillful mixture of gifted instructors, outstanding and smooth-read ipt materials, and a first-rate studying surroundings that actually shelve our ipt phase inside the pinnacle schooling’s rack. Our ipt allows theoretical standards to be bolstered with tremendous hands-on periods. Our ipt allows you to offer both standard and custom courses with a view to manage you from being a novice to an App-Maker (real time utility improvement).

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Benefits Of Attending The Inplant Training

Practical Experience. At the end of the Training you will be assisted on creating a project. Certificate and Software CD’s will be provided.

Our Curriculam

Section 1: Introduction (History Of Data Mining)

There is a colossal measure of information accessible in the Information Industry. This information is of no utilization until it is changed over into helpful data. It is important to dissect this colossal measure of information and concentrate valuable data from it. Extraction of data is not by any means the only procedure we have to perform; information mining likewise includes different procedures, for example, Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. When every one of these procedures are over, we would have the capacity to utilize this data in numerous applications, for example, Fraud Detection, Market Analysis, Production Control, Science Exploration, and so forth.

Data Mining is the process of analyzing data from different perspectives to discover relationships among separate data items. Data mining software is one of several different ways to analyze data and can be used for several different reasons. It can be used to cut costs, increase revenue or for both.

Class/Concept Description
Mining of Frequent Patterns
Mining of Associations
Mining of Correlations

Section 2: Data Mining Issues
Section 3: Data Mining Evaluation
Section 4: Data Warehousing (OLAP) To Data Mining (OLAM)
Section 5: Project Work

FREQUENTLY ASKED QUESTIONS

Group of similar objects that differ significantly from other objects.

 

Pruning is a technique in machine learning that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances.

 

Data Mining Decision Tree Induction. A decision tree is a structure that includes a root node, branches, and leaf nodes. Each internal node denotes a test on an attribute, each branch denotes the outcome of a test, and each leaf node holds a class label. The topmost node in the tree is the root node.

Models in Data mining help the different algorithms in decision making or pattern matching. The second stage of data mining involves considering various models and choosing the best one based on their predictive performance.

A data mining extension can be used to slice the data the source cube in the order as discovered by data mining. When a cube is mined the case table is a dimension.

Data mining extension is based on the syntax of SQL. It is based on relational concepts and mainly used to create and manage the data mining models. DMX comprises of two types of statements: Data definition and Data manipulation. Data definition is used to define or create new models, structures.

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