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48234 results in Computer Science

Page 675 of 2412

  • 2 - Introduction to Data Mining

  • Parteek Bhatia
  • Book:
    Data Mining and Data Warehousing
    Published online:
    26 April 2019
    Print publication:
    16 May 2019, pp 17-27

  • Summary

    Chapter Objectives

    ✓ To learn about the concepts of data mining.

    ✓ To understand the need for, and the applications of data mining

    ✓ To differentiate between data mining and machine learning

    ✓ To understand the process of data mining.

    ✓ To understand the difference between data mining and machine learning.

    Introduction to Data Mining

    In the age of information, an enormous amount of data is available in different industries and organizations. The availability of this massive data is of no use unless it is transformed into valuable information. Otherwise, we are sinking in data, but starving for knowledge. The solution to this problem is data mining which is the extraction of useful information from the huge amount of data that is available.

    Data mining is defined as follows:

    ‘Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. The patterns must be actionable so they may be used in an enterprise's decision making.’

    From this definition, the important take aways are:

  • • Data mining is a process of automated discovery of previously unknown patterns in large volumes of data.

  • • This large volume of data is usually the historical data of an organization known as the data warehouse.

  • • Data mining deals with large volumes of data, in Gigabytes or Terabytes of data and sometimes as much as Zetabytes of data (in case of big data).

  • • Patterns must be valid, novel, useful and understandable.

  • • Data mining allows businesses to determine historical patterns to predict future behaviour.

  • • Although data mining is possible with smaller amounts of data, the bigger the data the better the accuracy in prediction.

  • • There is considerable hype about data mining at present, and the Gartner Group has listed data mining as one of the top ten technologies to watch.

    • Need of Data Mining

    Data mining is a recent buzz word in the field of Computer Science. It is a computing process that uses intelligent mathematical algorithms to extract the relevant data and computes the probability of future actions. It is also known as Knowledge Discovery in Data (KDD).

  • 13 - Data Warehouse Schema

  • Parteek Bhatia
  • Book:
    Data Mining and Data Warehousing
    Published online:
    26 April 2019
    Print publication:
    16 May 2019, pp 405-415

  • Summary

    Chapter Objectives

    ✓ To understand the concept of dimension, measure and the fact table

    ✓ To able to apply different schema of data warehouse designs such as Star Schema, Snowflake Schema and Fact Constellation Schema on real world applications.

    ✓ To understand the differences between these schemas, their strengths and weakness.

    Introduction to Data Warehouse Schema

    Logical descriptions of database are known as Schema. It is the blueprint of the entire database. It defines how the data are organized and how the relations among them are associated. Data warehouse schema consists of the name and description of records including associated data items and aggregates. A database uses relational models whereas a data warehouse uses different types of schema, namely, Star, Snowflake, and Fact Constellation.

    To start discussion on these schemas, it is important to understand the basic terminology used in this process, which is discussed below.

    Dimension

    The term ‘dimension’ in data warehousing is a collection of reference information about a measurable event. These events are stored in a fact table and are known as facts. The dimensions are generally the entities for which an organization wants to preserve records. The descriptive attributes are organized as columns in dimension tables by a data warehouse. For example, a student's dimension attributes could consist of first and last name, roll number, age, gender, or an address dimension that would include street name, state, and country attributes.

    A dimension table consists of a primary key column that uniquely identifies each record (row) of dimension. A dimension is a framework that consists of one or more hierarchies that classify data. Usually dimensions are de-normalized tables and may have redundant data.

    Let us take a quick recap of the concepts of normalization and de-normalization, as they will be used in this chapter. Normalization is a process of breaking up a larger table into smaller tables free of any possible insertion, updation or deletion anomalies. Normalized tables have reduced redundancy of data. In order to get full information, these tables are usually joined.

    In de-normalization, smaller tables are merged to form larger tables to reduce joining operations. De-normalization is particularly performed in those cases where retrieval is a major requirement and insert, update, and delete operations are minimal, as in case of historical data or data warehouse. These de-normalized tables will have redundancy of data.

  • Challenges 60–68 - Turtle Graphics

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 51-57

  • Summary

    Drawing shapes and patterns with the Python turtle allows readers to practise using loops and helps them learn computational thinking skills where they have to spot repeating patterns. The nine challenges allow them to put these skills into practice.

  • Challenges 96–103 - 2D Lists and Dictionaries

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 79-85

  • Summary

    Building on their knowledge of lists, readers are shown how to create and amend two-dimensional lists and dictionaries. They complete nine challenges to practise creating two-dimensional lists and dictionaries, selecting specific rows, columns or individual values, adding new data or altering existing data.

  • 15 - Memory Hierarchy and Caching

  • 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 308-322

  • Summary

    Models of Memory Hierarchy

    Designing memory architecture is an important component of computer organization that tries to achieve a balance between computational speed and memory speed, viz., the time to fetch operands from memory. Computational speeds are much faster since the processing happens within the chip; whereas, a memory access could involve off chip memory units. To bridge this disparity, the modern computer has several layers of memory, called cache memory that provides faster access to the operands. Because of technological and cost limitations, cache memories offer a range of speed–cost tradeoffs. For example, the L1 cache, the fastest cache level is usually also of the smallest size. The L2 cache is larger, say by a factor of ten but also considerably slower. The secondary memory which is the largest in terms of size, e.g., the disk could be 10,000 times slower than the L1 cache. For any large size application, most of the data resides on disk and is transferred to the faster levels of cache when required.

    This movement of data is usually beyond the control of the normal programmer and managed by the operating system and hardware. By using empirical principles called temporal and spatial locality of memory access, several replacement policies are used to maximize the chances of keeping the operands in the faster cache memory levels. However, it must be obvious that there will be occasions when the required operand is not present in L1; one has to reach out to L2 and beyond and pay the penalty of higher access cost. In other words, memory access cost is not uniform as discussed in the beginning of this book but for simplicity of the analysis, we had pretended that it remains same.

    In this chapter, we will do away with this assumption; however, for simpler exposition, we will deal with only two levels of memory – slowand fast where the slower memory has infinite size while the faster one is limited, say, of size M and significantly faster. Consequently, we can pretend that the faster memory has zero (negligible) access cost and the slower memory has access cost 1.

  • Challenges 105–110 - Reading and Writing to a Text File

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 86-90

  • Summary

    Saving data to an external file is an essential skill allowing programs to have multiple practical real-world uses. This chapter shows the reader how to save data to text files as well as import data from text files into Python so it can be manipulated. By working through the six challenges the reader learns how to implement these important new skills.

  • 1 - Beginning with Machine Learning

  • Parteek Bhatia
  • Book:
    Data Mining and Data Warehousing
    Published online:
    26 April 2019
    Print publication:
    16 May 2019, pp 1-16

  • Summary

    Chapter Objectives

    ✓ To understand the concept of machine learning and its applications.

    ✓ To understand what are supervised and unsupervised machine learning strategies.

    ✓ To understand the concept of regression and classification.

    ✓ To identify the strategy to be applied to a given problem.

    Introduction to Machine Learning

    Machine Learning (ML) has emerged as the most extensively used tool for web-sites to classify surfers and address them appropriately. When we surf the Net, we are exposed to machine learning algorithms multiple times a day, often without realizing it. Machine learning is used by search engines such as Google and Bing to rank web pages or to decide which advertisement to show to which user. It is used by social networks such as Facebook and Instagram to generate a custom feed for every user or to tag the user by the picture that was uploaded. It is also used by banks to detect whether an online transaction is genuine or fraudulent and by e-commerce websites such as Amazon and Flipkart to recommend products that we are most likely to buy. Even email providers such as Gmail, Yahoo, and Hotmail use machine learning to decide which emails are spam and which are not. These are only a few examples of applications of machine learning.

    The ultimate aim of machine learning is to build an Artificial Intelligence (AI) platform that is as intelligent as the human mind. We are not very far from this dream and many AI researchers believe that this goal can be achieved through machine learning algorithms that try to mimic the learning processes of a human brain.

    Actually, ML is a branch of AI. Many years ago researchers tried to build intelligent programs with pre-defined rules like in the case of a normal program. But this approach did not work as there were too many special cases to be considered. For instance, we can define rules to find the shortest path between two points. But it is very difficult to make rules for programs such as photo tagging, classifying emails as spam or not spam, and web page ranking. The only solution to accomplish these tasks was to write a program that could generate its own rules by examining some examples (also called training data). This approach was named Machine Learning. This book will cover state of art machine learning algorithms and their deployment.

  • 3 - Beginning with Weka and R Language

  • Parteek Bhatia
  • Book:
    Data Mining and Data Warehousing
    Published online:
    26 April 2019
    Print publication:
    16 May 2019, pp 28-54

  • Summary

    Chapter Objectives

    ✓ To learn to install Weka and the R language

    ✓ To demonstrate the use of Weka software

    ✓ To experiment with Weka on the Iris dataset

    ✓ To introduce basics of R language

    ✓ To experiment with R on the Iris dataset

    About Weka

    In this book, all data mining algorithms are explained with Weka and R language. The learner can perform and apply these algorithms easily using these well-know data mining tool and language. Let's first discuss the Weka tool.

    Weka is an open-source software under the GNU General Public License System. It was developed by the Machine Learning Group, University of Waikato, New Zealand. Although named after a flightless New Zealand bird, ‘WEKA’ stands for Waikato Environment for Knowledge Analysis. The system is written using the object oriented language Java. Weka is data mining software and it is a set of machine learning algorithms that can be applied to a dataset directly, or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.

    The story of the development of Weka is very interesting. It was initially developed by students of University of Waikato, New Zealand, as part of their course work on data mining. They had implemented all major machine learning algorithms as part of lab work for this course. In 1993, the University of Waikato began development of the original version of Weka, which became a mix of Tcl/Tk, C, and Makefiles. In 1997, the decision was made to redevelop Weka from scratch in Java, including implementations of modeling algorithms. In 2006, Pentaho Corporation acquired an exclusive license to use Weka for business intelligence.

    This chapter will cover the installation of Weka, datasets available and will guide the learner about how to start experimentation using Weka. Later on we will discuss another data mining tool, R. Let us first discuss the installation process for Weka, step-by-step.

    Installing Weka

    Weka is freely available and its latest version can be easily downloaded from https://www.cs.waikato. ac.nz/ml/weka/downloading.html as shown in Figure 3.1.

    To work more smoothly, you must first download and install Java VM before downloading Weka.

  • Challenges 88–95 - Numeric Arrays

  • from Part I - Learning Python
  • Nichola Lacey
  • Book:
    Python by Example
    Published online:
    28 May 2019
    Print publication:
    16 May 2019, pp 72-78

  • Summary

    Readers learn about numeric arrays and data types such as integer, long, floating-point and double. They are shown how to import the array library, define an array in Python, append data, extend and combine arrays, remove items from an array along with sort and reverse arrays. They complete eight challenges to explore using numeric arrays in Python.

Page 675 of 2412