Skip to main content

Data structures: Introduction to Trees

Tree:The data in a tree are not stored in a sequential manner i.e., they are not stored linearly. Instead, they are arranged on multiple levels or we can say it is a hierarchical structure. For this reason, the tree is considered to be a non-linear data structure.
KeyConcepts:
Nodes: Individual units within the tree, each storing data and potentially linking to other nodes.
Edges: Connections between nodes, representing relationships (parent-child, sibling, etc.).
Root: The topmost node in the tree, from which all other nodes originate.
Parent: A node that has one or more child nodes.
Child: A node connected to a parent node.
Leaf: A node with no children.
Subtree: A portion of a tree that is itself a tree.
Representation of tree:
Binary search tree:

Comments

Post a Comment

Popular posts from this blog

DWDM lab exp2

  Explore machine learning tool“WEKA”//2.a// Explore WEKA Data Mining/Machine Learning Toolkit. ANS: WEKA(Waikato Environment for Knowledge Analysis) an open-source software provides tools for data preprocessing, implementation of several Machine Learning algorithms, and visualization tools so that we can develop machine learning techniques and apply them to real-world data mining problems. Features of WEKA - Preprocessor – Most of the Data is Raw. Hence, Preprocessor is used to clean the noisy data. Classify – After preprocessing the data, we assign classes or categories to items. Cluster – In Clustering, a dataset is arranged in different groups/clusters based on some similarities. Associate – Association rules highlight all the associations and correlations between items of a dataset. Select Attributes – Every dataset contains a lot of attributes; only significantly valuable attributes are selected for building a good model. Visualize – In Visualization, different plot matrices ...
Post a Comment
Read more

DWDM 2b ,2c experiment

 //2b// Downloading and/or installation of WEKA data mining toolkit. Go to the Weka website, http://www.cs.waikato.ac.nz/ml/weka/ , and download the software. Select the appropriate link corresponding to the version of the software based on your operating system and whether or not you already have Java VM running on your machine. The link will forward you to a site where you can download the software from a mirror site. Save the self-extracting executable to disk and then double click on it to install Weka. Answer yes or next to the questions during the installation. Click yes to accept the Java agreement if necessary. After you install the program Weka should appear on your start menu under Programs (if you are using Windows). Running Weka from the start menu select Programs, then Weka. You will see the Weka GUI Chooser. Select Explorer. The Weka Explorer will then launch. //2c// Understand the features of WEKA toolkit such as Explorer, Knowledge Flow interface, Experimenter, comm...
Post a Comment
Read more

Dwdm 1exp 2 bit

  1.experiment 2 . Design multi-dimensional data models namely Star, Snowflake and Fact Constellation schemas forany one enterprise (ex. Banking, Insurance, Finance, Healthcare, manufacturing, Automobiles, sales etc). What is Schema?  Schema is a logical description of the entire database. Star Schema:  A star schema is the elementary form of a dimensional model, in which data are organized into facts and dimensions .  This dimension table contains the set of attributes. The following diagram shows the sales data of a company with respect to the four dimensions, namely time, item, branch, and location .  There is a fact table at the center. It contains the keys to each of four dimensions .  Snowflake Schema:  Some dimension tables in the Snowflake schema are normalized.  The normalization splits up the data into additional tables.  Fact Constellation Schema:  A Fact constellation means two or more fact tables sharing one or more dim...
Post a Comment
Read more