CSDatawarehousing-and -DataMining · CSCharp-and-Dot-Net- Framework · CS System Software · CSArtificial-IntelligenceReg. Syllabus. DATA WAREHOUSING AND MINING UNIT-II DATA WAREHOUSING Data Warehouse Components, Building a Data warehouse, Mapping Data. To Download the Notes with Images Click HERE UNIT III DATA MINING Introduction – Data – Types of Data – Data Mining Functionalities.
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For example, interestingness measures for association rules include support and confidence.
cs2032 data warehouse and mining important question
In some cases, users may have no idea regarding what kinds of patterns in their data may be interesting, and hence may like to search for several different kinds of patterns in parallel. To study about the concepts and classification of Data mining systems. Loose coupling is better than no coupling because it can fetch any portion of data stored in databases or data warehouses by using query processing, indexing, and other system facilities. Data Warehousing and Data Mining.
Because data streams are normally not stored in any kind of data repository, effective and efficient management and analysis of stream data poses great challenges to researchers. Classification according to the kinds of knowledge mined: Get the quotation list. To study about the concepts and classification of Data mining systems. Two lines called whiskers outside the box extend to the smallest Minimum and largest Maximum observations.
Therefore, in this book, we choose to use the term data mining.
cs data warehouse and mining important question
Data Warehousing and Data Mining unibz J. From a database perspective on knowledge discovery, efficiency and scalability are key issues in the implementation of data mining systems. An ER data model represents the database as a set of entities and their relationships.
Note that this is an association between more than one attribute, or predicate i. Specialized storage and search techniques are also required.
It is used to store large amounts of data, such as analytics, historical, or customer data, and then build large reports and data mining against it. For example, we may be able to group the trends of fs2032 objects and identify some strangely moving vehicles, or distinguish a bioterrorist attack from a normal outbreak of the flu based on the geographic spread of a disease with time.
Object-relational databases are constructed based on an object-relational data model. Data evolution analysis describes and models regularities or trends for objects whose behavior changes ntoes time. Decision trees can easily be converted to classification rules. By performing data mining, interesting knowledge, regularities, or high-level information can be extracted from databases and viewed or browsed from different angles.
lecturer notes in cs2032
Suppose, as a marketing manager of AllElectronicsyou would like to. In other words, we can say that data mining is mining knowledge from data.
Designing a comprehensive data mining language is challenging because data mining covers a wide spectrum of tasks, from data characterization to evolution analysis. So this Important Questions may or may not come to examinations so concentrate more on these important questions, and also nltes things.
It may use interestingness thresholds to filter out discovered patterns. cs0232
CS Data Warehousing and Data Mining: Notes
That is, clusters of objects are formed so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Different applications often require the integration of application-specific methods.
The objects are clustered or grouped based on the principle of maximizing the intraclass similarity notss minimizing the interclass similarity. Therefore, a generic, all-purpose data mining system may not fit domain-specific mining tasks. Handling noisy or incomplete data: Data mining can be viewed as a result of the natural evolution of information technology. Transactions can be stored in a table, with one record per transaction.
Although the term prediction may refer to both numeric prediction and class label prediction. A time-series database stores sequences of values or events obtained over repeated measurements of time e.
For example, time may be decomposed according to fiscal years, academic years, or calendar years. These include data aggregation e.
The derived model is based on the analysis of a set of training data i. Cluster analysis can be performed on AllElectronics customer data in order to identify ih subpopulations of customers. Specific nltes mining systems should be constructed for mining specific kinds of data.
Data mining query languages and ad hoc data mining: These word descriptions are usually not simple keywords but rather long sentences or paragraphs, such as product specifications, error or bug reports, warning messages, summary reports, notes, or other documents. What Motivated Data Mining? Drilling down on a dimension, such as occupationor adding new dimensions, such as income levelmay help in finding even more discriminative features between the two classes.
Objects that share a common set of properties can be grouped into an object class. Data can be associated with classes or concepts. Unit 1 Data warehousing Email required Address never made public.
Both sum and count are distributive measures because they can be computed in this manner. Therefore, data mining is considered one of the most important frontiers in database and information systems and one of the most promising interdisciplinary developments in the information technology.
Database systems can be classified according to different criteria such as data models, or the types of data or applications involvedeach of which may require its own data mining technique.