This chapter covers indexing techniques ranging from the most basic one to highly specialized ones. Due to the extensive use of indices in database systems, this chapter constitutes an important part of a database course. A class that has already had a course on data-structures would likely be familiar with hashing and perhaps even B + -trees. However, this chapter is necessary reading even for those students since data structures courses typically cover indexing in main memory.
Appendix E - Hierarchical model. This chapter presents the following content: Basic concepts, tree-structure diagrams, data-retrieval facility, update facility, virtual records, mapping of hierarchies to files, the IMS database system.
Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.
Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations
Reduce the size of large data sets
Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction.
Given a set of transactions T, the goal of association rule mining is to find all rules having
support ≥ minsup threshold
confidence ≥ minconf threshold
List all possible association rules
Compute the support and confidence for each rule
Prune rules that fail the minsup and minconf thresholds
• Place where seed was collected
• Refers to natural forests, not plantations
• Provenance boundaries not always well defined
• Provenances may have different genetic adaptation. Subdivision of a species, with genetically similar individuals.
• Sometimes synonymous with provenance.
• Races separated using observed differences.
• For example, wood basic density, bark thickness, and flowering patterns have been used to define E. globulusraces
Chapter 11 - Indexing and hashing. This chapter covers indexing techniques ranging from the most basic one to highly specialized ones. Due to the extensive use of indices in database systems, this chapter constitutes an important part of a database course. A class that has already had a course on data-structures would likely be familiar with hashing and perhaps even B + -trees. However, this chapter is necessary reading even for those students since data structures courses typically cover indexing in main memory.
RClass*: A Prototype Rough-Set and Genetic Algorithms Enhanced Multi-Concept Classification System for Manufacturing Diagnosis 19.1 Introduction 19.2 Basic Notions 19.3 A Prototype Multi-Concept Classification System 19.4 Validation of RClass
19.5 Application of RClass
to Manufacturing Diagnosis
or classification of objects from large-scale empirical data sets is an important research
area in artificial intelligence (AI). In recent years, many techniques have been developed to perform
This book is intended for students of computer science at the college level,
or students of other subjects that cover Artificial Intelligence. It also is
intended to be an interesting and relevant introduction to the subject for
other students or individuals who simply have an interest in the subject.
The book assumes very little knowledge of computer science, but does
assume some familiarity with basic concepts of algorithms and computer
Divided into three separate sections, C & Data Structures covers C programming, as well as the implementation of data structures and an analysis of advanced data structure problems. Beginning with the basic concepts of the C language (including the operators, control structures, and functions), the book progresses to show these concepts through practical application with data structures such as linked lists and trees, and concludes with the integration of C programs and advanced data structure problem-solving. The book covers a vast ...
Chapter 3 - Essential documentation. After completing Chapter 3, the students will be able to: Describe the basic features of SpringCharts EHR, describe the history of SpringCharts EHR, apply user preferences, carry out setting up and editing patients, use pop-up text, explain the concept of an electronic chart, use the electronic chart’s face sheet, use the SpringCharts EHR care tree.