Algorithms and Complexity
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Read MoreJava Programming Examples: 2D Arrays, Recursion, Interfaces, Loops, and HashMaps
2D Arrays
Getting a Column from a 2D Array
The getCol method takes a 2D integer array (nos) and an integer (col) representing the column index as input. It returns an integer array containing the elements of the specified column. If the col value is out of bounds, it throws an Exception with the message “invalid column.”
public static int[] getCol(int[][] nos, int col) throws Exception {
if (col < 0 || col >= nos[0].length) {
throw new Exception("invalid column");
}
int[]Database Design
Day 1
Data
- Raw, unorganized facts
- (Unformatted Data)
Information
- Organized data with context
- (Formatted Data)
Metadata
- DATA that describes your DATA
- The “lens” we use to understand what data means
- Describes properties of data so we can infer information
Day 2
“Old” Way Process of Filing Systems
- Data stored in files managed by the application
- EX:
- STUDENT Files <-> Student Processing Applications <-> Users
- FACULTY and STAFF Files <-> Faculty and Staff Processing Applications <-> Users
- ALUMNI
Understanding Computer Architecture and Number Systems
Base System Numbers
– Binary (Base 2): 0, 1
– Octal (Base 8): 0, 1, 2, 3, 4, 5, 6, 7
– Decimal (Base 10): 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
– Hexadecimal (Base 16): 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F
Decimal to Other Number Systems
To convert a decimal number to another base, repeatedly divide the decimal number by the desired base (e.g., 16 for hexadecimal, 8 for octal). The remainders, written in reverse order, form the converted number. Don’t forget to include the base at the end.
Other Number
Read MoreComprehensive Guide to Clustering, Anomaly Detection, and Stream Mining in Data Mining
Clustering
What is Clustering?
Clustering is an unsupervised machine learning technique used to group similar data points into clusters, making it easier to analyze and interpret large datasets. There are several methods of clustering, each with its own approach and use cases.
1. Partitioning Methods
K-Means Clustering:
Concept: This method partitions the dataset into K clusters, where each data point belongs to the cluster with the nearest mean (centroid).
Algorithm:
- Initialize K centroids randomly.
- Assign