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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Data mining involves an integration of techniques from multiple disciplines such as database and data warehouse technology, statistics, machine learning, high-performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial or temporal data analysis.
Although this may include characterization, discrimination, association and correlation analysis, classification, prediction, or clustering of time related data, distinct features of such an analysis include time-series data analysis. The resulting classification should maximally distinguish each class from the others, presenting an organized picture of the data set.
We agree that data mining is a step in the knowledge discovery process. The student should be made to: CS Unit I Notes.
The data notss primitives specify the following, as illustrated in Figure 1. Several objective measures of pattern interestingness exist. Because different users can be interested in different kinds of knowledge, data mining should cover a wide spectrum of data analysis and knowledge discovery tasks, including data characterization, discrimination, association and correlation analysis, classification, prediction, clustering, outlier analysis, and evolution analysis which includes trend and similarity analysis.
Therefore, in this book, we choose to use the term data mining. Typically, the ends of the box are at the quartiles, so that the box length is the interquartile range, IQR. Fundamentals of data mining. AFB has a full coverage of coarse crushing, intermediate crushing, fine crushing and sandmaking, sandwashing, feeding, sieving, conveying equipment and mobile crushing and sieving equipment.
A holistic measure is a measure that must be computed on the entire data set as a whole. Three clusters of data points are evident. Relational data can be accessed by database queries written in a relational query language, such as SQL, or with the assistance of graphical user interfaces.
Data Warehousing and Data Mining Chapter 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 ij. Domain knowledge related to databases, such as integrity constraints and deduction rules, can help focus and speed up a data mining process, or judge the interestingness of discovered patterns. Explore Our Products Here.
Variance and Standard Deviation The variance of N observations, x 1; x 2;: Specific data mining systems should be constructed for mining specific kinds of data.
These primitives allow the user to interactively communicate with the data mining system during discovery in order to direct the mining process, or examine the findings from different angles or depths. Atypical query model in such a system is the continuous query lnwhere predefined queries constantly evaluate incoming streams, collect aggregate data, report the current status of data streams, and respond to their changes.
The components communicate in order to exchange information and answer queries. It can be useful to describe individual classes and concepts in summarized, concise, and yet precise terms. Such a decision tree may help you understand the impact of the given sales campaign and design a more effective campaign for the future.
Specialized storage and search techniques are also required.
lecturer notes in cs2032
A decision tree is a flow-chart-like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the nites, and tree leaves represent classes or class distributions. Data transformation operations, such motes normalization and aggregation, are additional data preprocessing procedures that would contribute toward the success of the mining process.
Suppose, instead, that we are given the AllElectronics relational database relating to purchases. Each object has associated with it the following:. It focuses on selected subjects, and thus its scope is department-wide. Attributes of interest may not always be available, such as customer information for sales transaction data.
Data Warehousing and Data Mining CS notes – Annauniversity lastest info
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. The AllElectronics company is described by the following relation tables: Because most relational database systems do not support nested relational structures, the transactional database is usually either stored in a flat file in a format similar to that of the table in Figure 1.
Pattern evaluation —the interestingness problem: This is the domain knowledge that is used to guide the search or evaluate the interestingness of resulting patterns. This is especially crucial if the data mining system is to be interactive. This is a difficult task, particularly since the relevant data are spread out over several databases, physically located at numerous sites. 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.