Additional Info
  • Publisher: Laxmi Publications
  • Language: English
  • ISBN : 978-93-83828-40-1
  • Chapter 1

    Introduction Price 2.99  |  2.99 Rewards Points

  • Chapter 2

    data Mining Price 2.99  |  2.99 Rewards Points

  • Chapter 3

    Data Warehouse Price 2.99  |  2.99 Rewards Points

    Data warehouse
  • Chapter 4

    Data Preprocessing Price 2.99  |  2.99 Rewards Points

    Data Preprocessing is essential part of work to improve the efficiency and ease of the data mining process. Real-world data is dirty. Dirty data can cause confusion for the mining procedure, resulting in unreliable output. What is dirty data ? The data which is Incomplete, Noisy, and Inconsistent, that data is called as dirty data. The data is dirty due to their typically huge size, often several gigabytes or more and other kind of reasons.
  • Chapter 5

    Data Mining Primitives And Dmql Price 2.99  |  2.99 Rewards Points

    INTRODUCTION Data mining system can dig lot of patterns from the given large databases. Most of the patterns discovered would be irrelevant to the analysis task. Furthermore, many of the patterns found, may related to the analysis task, but difficult to understand or lack validity, novelty, utility, it make them uninteresting. A more realistic scenario is to expect that users can communicate with the data mining system using a set of data mining primitives designed in order to facilitate efficient and fruitful knowledge discovery. The primitives of data mining system allow the users to interactively communicate with the data mining system during discovery in order to examine the findings from different angles or depths, and direct the mining process. In this chapter we will learn about data mining primitives and study the designing of a data mining query language based on data mining primitive principles. A data mining query language can be designed to incorporate data mining primitives, allowing users to flexibly interact with data mining systems. Data mining query language provides a foundation on user-friendly graphical interfaces can be built.
  • Chapter 6

    A Ssociation Rules Mining Price 2.99  |  2.99 Rewards Points

    Among the areas of data mining, the problem of deriving associations from data has received a great deal of attention. A huge amount of data stored electronically in most enterprises. In particular, in all retail outlets the amount of data stored has grown enormously. In this, we are given a set of items and a large collection of transactions, which are subsets of these items. The task is to find relationships between the presences of various items within these baskets.
  • Chapter 7

    Classification Price 2.99  |  2.99 Rewards Points

    If we think for a minute about how we classify common everyday objects such as people and cars, itÂ’s pretty clear that we are using features of those objects to do the job. People have legs, which are a feature that cars donÂ’t have. Cars have wheels that are a feature that people donÂ’t have. By selecting the appropriate set of features , we can do a good job of classification. To make this kind of feature-based classification work, we need to have some knowledge of what features make good predictors of class membership for the classes we are trying to distinguish. For example, having wheels or not distinguishes people from cars, but doesnÂ’t distinguish cars from trains. These are two different classification tasks. Depending on the classification task we are facing, different features or sets of features may be important, and knowing how we arrive at our knowledge of which features are useful to which task is essential.
  • Chapter 8

    Cluster Analysis Price 2.99  |  2.99 Rewards Points

    Clustering is a division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Representing data by fewer clusters necessarily loses certain fine details (akin to lossy data compression), but achieves simplification. It represents many data objects by few clusters, and hence, it models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept.
  • Chapter 9

    Data Mining For Unstructured Types Of Data Price 2.99  |  2.99 Rewards Points

    So far we focused on data mining techniques on relational databases, transactional databases and data warehouse formed by the transformation and integration of structured data. Large amount of data in various complex forms like hypertext, multimedia, geographical, time series and text data have been growing rapidly for industrial transactions. Therefore, an increasingly important task in data mining is to mine complex types of data, including complex objects, spatial data, multimedia data, time-series data, text data, and the World Wide Web.
  • Chapter 10

    Introduction To Soft Computing Techniques Price 2.99  |  2.99 Rewards Points

    Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost.
  • Chapter 11

    Artificial Neural Networks Price 2.99  |  2.99 Rewards Points

    For many decades, it has been a goal of science and engineering to develop intelligent machines with a large number of simple elements. In 1940s, researchers desiring to duplicate the function of the human brain, have developed simple hardware (and later software) models of biological neurons and their interaction systems

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