Data Mining And Data Warehousing Notes Pdf Uptu

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cs data warehousing and data mining model question paper

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To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Krishna Priya. Download PDF. A short summary of this paper. Why is it important? The major reason that data mining has attracted a great deal of attention in information industry in recent years is due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from business management, production control, and market analysis, to engineering design and science exploration.

The evolution of database technology Data collection and Database Creation s and earlier Primitive file processing Database Management Systems s-early s 1 Hierarchical and network database system 2 Relational database system 3 Data modeling tools: entity-relational models, etc 4 Indexing and accessing methods: B-trees, hashing etc.

Data mining refers to extracting or mining" knowledge from large amounts of data. Based on this view, the architecture of a typical data mining system may have the following major components: 1. A database, data warehouse, or other information repository, which consists of the set of databases, data warehouses, spreadsheets, or other kinds of information repositories containing the student and course information. A knowledge base that contains the domain knowledge used to guide the search or to evaluate the interestingness of resulting patterns.

For example, the knowledge base may contain metadata which describes data from multiple heterogeneous sources. A data mining engine, which consists of a set of functional modules for tasks such as classification, association, classification, cluster analysis, and evolution and deviation analysis. A pattern evaluation module that works in tandem with the data mining modules by employing interestingness measures to help focus the search towards interestingness patterns.

A graphical user interface that allows the user an interactive approach to the data mining system. How is a data warehouse different from a database? How are they similar? There could be multiple heterogeneous databases where the schema of one database may not agree with the schema of another. A database system supports ad-hoc query and on-line transaction processing. In principle, data mining should be applicable to any kind of information repository.

This includes relational databases, data warehouses, transactional databases, advanced database systems, flat files, and the World-Wide Web. Advanced database systems include object-oriented and object-relational databases, and special c application-oriented databases, such as jntuworldupdates.

Flat files: Flat files are actually the most common data source for data mining algorithms, especially at the research level. Flat files are simple data files in text or binary format with a structure known by the data mining algorithm to be applied. The data in these files can be transactions, time-series data, scientific measurements, etc.

Relational Databases: a relational database consists of a set of tables containing either values of entity attributes, or values of attributes from entity relationships. Tables have columns and rows, where columns represent attributes and rows represent tuples.

A tuple in a relational table corresponds to either an object or a relationship between objects and is identified by a set of attribute values representing a unique key. In following figure it presents some relations Customer, Items, and Borrow representing business activity in a video store. These relations are just a subset of what could be a database for the video store and is given as an example.

The most commonly used query language for relational database is SQL, which allows retrieval and manipulation of the data stored in the tables, as well as the calculation of aggregate functions such as average, sum, min, max and count.

Data mining algorithms using relational databases can be more versatile than data mining algorithms specifically written for flat files, since they can take advantage of the structure inherent to relational databases. While data mining can benefit from SQL for data selection, transformation and consolidation, it goes beyond what SQL could provide, such as jntuworldupdates. Data warehouses A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and which usually resides at a single site.

Data warehouses are constructed via a process of data cleansing, data transformation, data integration, data loading, and periodic data refreshing.

The figure shows the basic architecture of a data warehouse. In order to facilitate decision making, the data in a data warehouse are organized around major subjects, such as customer, item, supplier, and activity.

The data are stored to provide information from a historical perspective and are typically summarized. A data warehouse is usually modeled by a multidimensional database structure, where each dimension corresponds to an attribute or a set of attributes in the schema, and each cell stores the value of some aggregate measure, such as count or sales amount.

The actual physical structure of a data warehouse may be a relational data store or a multidimensional data cube. It provides a multidimensional view of data and allows the precomputation and fast accessing of summarized data.

Its corresponding higher level multidimensional cube structures are called non-base cuboids. A base cuboid together with all of its corresponding higher level cuboids form a data cube. By providing multidimensional data views and the precomputation of summarized data, data warehouse systems are well suited for On-Line Analytical Processing, or OLAP. OLAP operations make use of background knowledge regarding the domain of the data being studied in order to allow the presentation of data at different levels of abstraction.

Such operations accommodate different user viewpoints. Examples of OLAP operations include drill-down and roll-up, which allow the user to view the data at differing degrees of summarization, as illustrated in above figure.

Transactional databases jntuworldupdates. Each entity in the database is considered as an object. The object contains a set of variables that describe the object, a set of messages that the object can use to communicate with other objects or with the rest of the database system and a set of methods where each method holds the code to implement a message. Raster data consists of n-dimensional bit maps or pixel maps, and vector data are represented by lines, points, polygons or other kinds of processed primitives, Some examples of spatial databases include geographical map databases, VLSI chip designs, and medical and satellite images databases.

These databases usually have a continuous flow of new data coming in, which sometimes causes the need for a challenging real time analysis. Data mining in such databases commonly includes the study of trends and correlations between evolutions of different variables, as well as the prediction of trends and movements of the variables in time. Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. Predictive mining tasks perform inference on the current data in order to make predictions.

Describe data mining functionalities, and the kinds of patterns they can discover or Define each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classification, prediction, clustering, and evolution analysis.

Give examples of each data mining functionality, using a real-life database that you are familiar with. It describes a given set of data in a concise and summarative manner, presenting interesting general properties of the data. These descriptions can be derived via 1. Data Discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes.

Example jntuworldupdates. The output of data characterization can be presented in various forms. Examples include pie charts, bar charts, curves, multidimensional data cubes, and multidimensional tables, including crosstabs. The resulting descriptions can also be presented as generalized relations, or in rule form called characteristic rules.

Discrimination descriptions expressed in rule form are referred to as discriminant rules. Example: A grocery store retailer to decide whether to but bread on sale. To help determine the impact of this decision, the retailer generates association rules that show what other products are frequently purchased with bread.

Based on these facts, he tries to capitalize on the association between bread, pretzels, and jelly by placing some pretzels and jelly at the end of the aisle where the bread is placed. In addition, he decides not to place either of these items on sale at the same time. The derived model is based on the analysis of a set of training data i.

Example: An airport security screening station is used to deter mine if passengers are potential terrorist or criminals. To do this, the face of each passenger is scanned and its basic pattern distance between eyes, size, and shape of mouth, head etc is identified. Although prediction may refer to both data value prediction and class label prediction, it is usually confined to data value prediction and thus is distinct from classification.

Prediction also encompasses the identification of distribution trends based on the available data. Example: Predicting flooding is difficult problem. One approach is uses monitors placed at various points in the river. These monitors collect data relevant to flood prediction: water level, rain amount, time, humidity etc. These water levels at a potential flooding point in the river can be predicted based on the data collected by the sensors upriver from this point.

The prediction must be made with respect to the time the data were collected. Classification vs. Prediction Classification differs from prediction in that the former is to construct a set of models or functions that describe and distinguish data class or concepts, whereas the latter is to predict some missing or unavailable, and often numerical, data values.

Their similarity is that they are both tools for prediction: Classification is used for predicting the class label of data objects and prediction is typically used for predicting missing numerical data values.

The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Each cluster that is formed can be viewed as a class of objects. To determine the target mailings of the various catalogs and to assist in the creation of new, more specific catalogs, the company performs a clustering of potential customers based on the determined attribute values.

The results of the clustering exercise are the used by management to create special catalogs and distribute them to the correct target population based on the cluster for that catalog. These data objects are outliers. In other words, the data objects which do not fall within the cluster will be called as outlier data objects. Noisy data or exceptional data are also called as outlier data. The analysis of outlier data is referred to as outlier mining. Example Outlier analysis may uncover fraudulent usage of credit cards by detecting purchases of jntuworldupdates.

Outlier values may also be detected with respect to the location and type of purchase, or the purchase frequency. Example: The data of result the last several years of a college would give an idea if quality of graduated produced by it Correlation analysis Correlation analysis is a technique use to measure the association between two variables.

AKTU PAPER: DATA WAREHOUSING & DATA MINING (MTCS024) 2018-19

Course Duration: Bachelor of Technology [B. Tech] Computer Science and Engineering is 4 Years. Updated on - Mar 05th, PM by Vyshnav. Tech Computer Engineering program is a 4 years undergraduate technology program that has eight semesters. B Tech Computer Engineering syllabus is designed to train aspirants to develop computer software and aided designs. The second to fourth semester covers the core subjects.

DATA WAREHOUSING AND MINING (RCAE13)

Data Warehousing is the process of extracting and storing data to allow easier reporting Whereas Data mining is the use of pattern recognition logic to identify trends within a sample data set a typical use of data mining is to identify fraud and to flag unusual patterns in behavior. Identify data mining problems and implement the data warehouse Write association rules for a given data pattern Choose between classification and clustering solution Question paper pattern The question paper will have TEN questions There will be TWO questions from each module. A process to reject data from the data warehouse and to create the necessary indexes B A process to load the data in the data warehouse and to create the necessary indexes C A process to upgrade the quality of data after it is moved into a data warehouse D A process to upgrade the quality of data before it is moved into a data warehouse.

Data Mining Classification: Basic Concepts and Techniques Lecture Notes for Chapter 3 Introduction to Data mining technology is something that helps one person in their decision making and that decision making is a process wherein which all the factors of mining is involved precisely. If you have any query then you can comment below and we will get back to you as soon as possible. Defined in many different ways, but not. It violates user privacy: 12 hours ago Delete Reply Block. Comment goes here.

Data Warehousing is the process of extracting and storing data to allow easier reporting Whereas Data mining is the use of pattern recognition logic to identify trends within a sample data set a typical use of data mining is to identify fraud and to flag unusual patterns in behavior. Identify data mining problems and implement the data warehouse Write association rules for a given data pattern Choose between classification and clustering solution Question paper pattern The question paper will have TEN questions There will be TWO questions from each module. A process to reject data from the data warehouse and to create the necessary indexes B A process to load the data in the data warehouse and to create the necessary indexes C A process to upgrade the quality of data after it is moved into a data warehouse D A process to upgrade the quality of data before it is moved into a data warehouse.

UPTU B. Tech. (SEM. VIII) EXAMINATION, 2006-07, DATA MINING & WAREHOUSING

Bellaachia Page: 4 2. Technical interview questions and answers interview FAQ. This ebook is extremely useful. Department of Information Technology. Data mining and data warehousing lecture notes for mca pdf.

Workforce Statistics. This section provides information relating to employment in warehousing and storage. These data are obtained from employer or establishment surveys. Differences Between Data Warehousing vs. Data Mining Data is reposited in fact table and dimension table. Fact table consists of data about transaction and dimensional table consists of master data.

Data Warehousing & Data Mining Sample Papers BTech IT 7th Semester

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  1. Krin R. 28.01.2021 at 12:05

    Bachelor of Computer Science & Engineering 3rd Year (V -VI Semester) Aktu Lecture Notes.6th DMDW (Data Mining And DataWareHousing).