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48196 results in Computer Science

Page 672 of 2410

  • Preface

  • Parteek Bhatia
  • Book:
    Data Mining and Data Warehousing
    Published online:
    26 April 2019
    Print publication:
    16 May 2019, pp xxxi-xxxii

  • Summary

    In the modern age of artificial intelligence and business analytics, data is considered as the oil of this cyber world. The mining of data has huge potential to improve business outcomes, and to carry out the mining of data there is a growing demand for database mining experts. This book intends training learners to fill this gap.

    This book will give learners sufficient information to acquire mastery over the subject. It covers the practical aspects of data mining, data warehousing, and machine learning in a simplified manner without compromising on the details of the subject. The main strength of the book is the illustration of concepts with practical examples so that the learners can grasp the contents easily. Another important feature of the book is illustration of data mining algorithms with practical hands-on sessions on Weka and R language (a major data mining tool and language, respectively). In this book, every concept has been illustrated through a step-by-step approach in tutorial form for self-practice in Weka and R. This textbook includes many pedagogical features such as chapter wise summary, exercises including probable problems, question bank, and relevant references, to provide sound knowledge to learners. It provides the students a platform to obtain expertise on technology, for better placements.

    Video sessions on data mining, machine learning, big data and DBMS are also available on my YouTube channel. Learners are requested to subscribe to this channel https://www.youtube.com/user/parteekbhatia to get the latest updates through video sessions on these topics.

    Your suggestions for further improvements to the book are always welcome. Kindly e-mail your suggestions to parteek.bhatia@gmail.com.

    I hope you enjoy learning from this book as much as I enjoyed writing it.

  • 7 - Cluster Analysis

  • Parteek Bhatia
  • Book:
    Data Mining and Data Warehousing
    Published online:
    26 April 2019
    Print publication:
    16 May 2019, pp 155-205

  • Summary

    Chapter Objectives

    ✓ To comprehend the concept of clustering, its applications, and features.

    ✓ To understand various distance metrics for clustering of data.

    ✓ To comprehend the process of K-means clustering.

    ✓ To comprehend the process of hierarchical clustering algorithms.

    ✓ To comprehend the process of DBSCAN algorithms.

    Introduction to Cluster Analysis

    Generally, in the case of large datasets, data is not labeled because labeling a large number of records requires a great deal of human effort. The unlabeled data can be analyzed with the help of clustering techniques. Clustering is an unsupervised learning technique which does not require a labeled dataset.

    Clustering is defined as grouping a set of similar objects into classes or clusters. In other words, during cluster analysis, the data is grouped into classes or clusters, so that records within a cluster (intra-cluster) have high similarity with one another but have high dissimilarities in comparison to objects in other clusters (inter-cluster), as shown in Figure 7.1.

    The similarity of records is identified on the basis of values of attributes describing the objects. Cluster analysis is an important human activity. The first human beings Adam and Eve actually learned through the process of clustering. They did not know the name of any object, they simply observed each and every object. Based on the similarity of their properties, they identified these objects in groups or clusters. For example, one group or cluster was named as trees, another as fruits and so on. They further classified the fruits on the basis of their properties like size, colour, shape, taste, and others. After that, people assigned labels or names to these objects calling them mango, banana, orange, and so on. And finally, all objects were labeled. Thus, we can say that the first human beings used clustering for their learning and they made clusters or groups of physical objects based on the similarity of their attributes.

    Applications of Cluster Analysis

    Cluster analysis has been widely used in various important applications such as:

  • • Marketing: It helps marketers find out distinctive groups among their customer bases, and this knowledge helps them improve their targeted marketing programs.

  • • Land use: Clustering is used for identifying areas of similar land use from the databases of earth observations.

  • • Insurance: Clustering is helpful for recognizing clusters of insurance policyholders with a high regular claim cost.

  • Challenge 146 - Shift Code

  • from Part II - Chunky Challenges
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 150-152

  • Summary

    This is the first of the five chunky challenges that are created to allow the reader to practice all the skills they have learnt in a larger program that will take longer to complete. In this challenge the reader must create a program that will encode or decode a message using a shift code. The skills they will need include: input and display data, lists, splitting and joining strings, if statements, loops (while and for) and subprograms.

  • 6 - Implementing Classification in Weka and R

  • Parteek Bhatia
  • Book:
    Data Mining and Data Warehousing
    Published online:
    26 April 2019
    Print publication:
    16 May 2019, pp 128-154

  • Summary

    Chapter Objectives

    ✓ To demonstrate the use of the decision tree

    ✓ To apply the decision tree on a sample dataset

    ✓ To implement a decision tree process using Weka and R

    Building a Decision Tree Classifier in Weka

    In this chapter, we will learn how Weka's decision tree feature helps to classify unknown samples of a dataset based on its attribute values. When Weka's decision tree is applied to an unknown sample, the decision tree classifies the sample into different classes such as Class A, Class B and Class C as shown in Figure 6.1.

    For example, if we want to predict the class of an unknown sample of a flower based on the length and width dimensions of its Sepal and Petal. The first step would be to measure Sepal length and width and Petal length and width of an unknown flower and compare these dimensions to the values of the samples in our dataset of known species. The decision tree algorithm of Weka will help in creating decision rules to predict the class of unknown flower automatically as shown in Figure 6.2.

    As shown in Figure 6.2, the dimensions of an unknown sample of flower will be matched with the rules generated by the decision tree. First, the rules will be matched to determine whether the sample belongs to Setosa class or not, if yes, the unknown sample will be classified as setosa. If not, the unknown sample will be checked for being of the Virginica class. If it matches with the conditions of the Virginica class, it will be labeled as Virginica, otherwise Versicolor. It is important to note that it would not be simple to create these rules on the basis of the values of single attribute as shown in Table 6.1. It is clear that for the same Sepal width, the flower may be of Setosa or Versicolor or Virginica, making it unclear which species an unknown flower belongs to on the basis of Sepal width alone. Thus, the decision tree must make its prediction based on all four flower dimensions.

    Due to such overlaps, the decision tree cannot predict with 100% accuracy the class of flower, but can only determine the likelihood of an unknown sample belonging to a particular class. In real situations the decision tree algorithm works on the basis of probability.

  • Challenges 52–59 - Random

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 45-50

  • Summary

    Generating random integers and selecting random values from a list shows readers how to create simple games and a basic maths quiz. The eight challenges help them experiment with the Python language and create workable solutions to improve their programming skills.

  • Challenges 27–34 - Maths

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 31-34

  • Summary

    The reader is introduced to the maths library and several mathematical operators including addition, subtraction, multiplication, division, whole number division, finding the remainders, using pi and calculating the square root of a number. They also look at the difference between integers and floating point numbers and use rounding to display number to a set number of decimal places as they complete the eight challenges in this chapter.

  • Challenges 139–145 - SQLite

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 134-146

  • Summary

    SQL databases are widely used in industry and this final chapter of Part I teaches the reader how to link to an SQL database using SQLite 3. It explains what a relational database is, the use of primary keys and it shows how to create a table, import the data into Python and run selection queries. The seven challenges allow the reader to put these new skills into practice.

  • 14 - Parallel Algorithms

  • Sandeep Sen, Indian Institute of Technology, Delhi, Amit Kumar, Indian Institute of Technology, Delhi
  • Book:
    Design and Analysis of Algorithms
    Published online:
    27 April 2019
    Print publication:
    16 May 2019, pp 277-307

  • Summary

    Models of Parallel Computation

    There is a perpetual need for faster computation which is unlikely to be ever satisfied. With device technologies hitting physical limits, alternate computational models are being explored. The Big Data phenomenon precedes the coinage of this term by many decades. One of the earliest and natural direction to speed-up computation was to deploy multiple processors instead of a single processor for running the same program. The ideal objective is to speed-up a program p-fold by using p processors simultaneously. A common caveat is that an egg cannot be boiled faster by employing multiple cooks! Analogously, a program cannot be executed faster indefinitely by using more and more processors. This is not just because of physical limitations but dependencies between various fragments of the code, imposed by precedence constraints.

    At a lower level, namely, in digital hardware design, parallelism is inherent – any circuit can be viewed as a parallel computational model. Signals travel across different paths and components and combine to yield the desired result. In contrast, a program is coded in a very sequential manner and the data flows are often dependent on each other – just think about a loop that executes in a sequence. Second, for a given problem, one may have to re-design a sequential algorithm to extract more parallelism. In this chapter, we focus on designing fast parallel algorithms for fundamental problems.

    A very important facet of parallel algorithm design is the underlying architecture of the computer, viz., how do the processors communicate with each other and access data concurrently. Moreover, is there a common clock across which we can measure the actual running time? Synchronization is an important property that makes parallel algorithm design somewhat more tractable. In more generalized asynchronous models, there are additional issues like deadlock and even convergence, which are very challenging to analyze.

    In this chapter, we will consider synchronous parallel models (sometimes called SIMD) and look at two important models – parallel random access machine (PRAM) and the interconnection network model. The PRAM model is the parallel counterpart of the popular sequential RAM model where p processors can simultaneously access a common memory called shared memory.

  • Challenges 118–123 - Subprograms

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 99-109

  • Summary

    In many programming languages subprograms incorporate both functions and procedures, however, in Python these are combined into a simple single subprogram and it is not necessary to define the type. The reader is shown how to call subprograms, return values from a subprogram and use variables in subprograms. They complete six challenges and use menus to make the programs user-friendly.

  • Challenges 124–132 - Tkinter GUI

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 110-123

  • Summary

    Tkinter allows the reader to create a graphical user interface (GUI) that incorporates windows, buttons, text boxes and lists boxes. The nine challenges allow the reader to experiment with layouts they like to create a more user-friendly interface.

  • Challenges 1–11 - The Basics

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 11-16

  • Summary

    The user starts to experiment and use the example code to write simple coded solutions using Python. They learn how to incorporate input and print statements and combine strings and variables to produce meaningful programs. There are eleven challenges to complete and the answers are provided for each challenge given.

Page 672 of 2410