Compare and contrast the behavior of bubble sort, insertion sort on the following set of value 1,2,3,4,5,6 Data structure using c++


Compare and contrast the  behavior  of bubble sort, insertion sort  on the  following set of value

      1,2,3,4,5,6


Data structure using c++

Let's compare and contrast the behavior of bubble sort and insertion sort on the set of values {1, 2, 3, 4, 5, 6} using C++.


Bubble Sort:

Bubble sort is a simple sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. The algorithm continues to pass through the list until no more swaps are needed, indicating that the list is sorted.


Behavior:


In the first pass, the largest element (6) will "bubble" up to the last position.

In the second pass, the second largest element (5) will move to the second-to-last position.

This process continues until all elements are in their correct sorted positions.

Step-by-step behavior of bubble sort on {1, 2, 3, 4, 5, 6}:

Pass 1: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 2: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 3: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 4: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 5: [1, 2, 3, 4, 5, 6] (no swaps)


Bubble sort has a time complexity of O(n^2) in the worst and average cases, and O(n) in the best case when the list is already sorted.


Insertion Sort:

Insertion sort is another simple sorting algorithm that builds the final sorted array one item at a time. It iterates through the input elements and, for each element, places it in its correct position within the already sorted portion of the array.


Behavior:


Initially, the first element (1) is considered as the sorted portion.

The algorithm then picks the second element (2) and inserts it into the correct position within the sorted portion (1).

It continues this process for the remaining elements, gradually expanding the sorted portion of the array.

Step-by-step behavior of insertion sort on {1, 2, 3, 4, 5, 6}:

Pass 1: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 2: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 3: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 4: [1, 2, 3, 4, 5, 6] (no swaps)

Pass 5: [1, 2, 3, 4, 5, 6] (no swaps)


Insertion sort has a time complexity of O(n^2) in the worst and average cases, and O(n) in the best case when the list is already sorted.


Comparison and Contrast:

Both bubble sort and insertion sort have similar behavior on the given set of values. Since the input array is already sorted in ascending order, no swaps are required in either algorithm. Therefore, they both perform optimally, with a time complexity of O(n) in this specific case.


However, in the general case, bubble sort and insertion sort differ in terms of their average and worst-case time complexity. Bubble sort always performs Θ(n^2) comparisons and swaps, regardless of the input. On the other hand,







Comments

Post a Comment

Popular posts from this blog

Load a Pandas dataframe with a selected dataset. Identify and count the missing values in a dataframe. Clean the data after removing noise as follows: a. Drop duplicate rows. b. Detect the outliers and remove the rows having outliers c. Identify the most correlated positively correlated attributes and negatively correlated attributes

  Load a Pandas dataframe with a selected dataset. Identify and count the missing values in a dataframe. Clean the data after removing noise as follows: a. Drop duplicate rows. b. Detect the outliers and remove the rows having outliers c. Identify the most correlated positively correlated attributes and negatively correlated attributes CODE import pandas as pd # Load the dataset df = pd.read_csv('your_dataset.csv') # Identify and count missing values missing_values = df.isnull().sum() print("Missing values:") print(missing_values) # Drop duplicate rows df = df.drop_duplicates() # Detect outliers and remove rows with outliers def remove_outliers(df, column):     Q1 = df[column].quantile(0.25)     Q3 = df[column].quantile(0.75)     IQR = Q3 - Q1     lower_bound = Q1 - 1.5 * IQR     upper_bound = Q3 + 1.5 * IQR     return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)] columns_to_check = ['column1', 'c...
Read more

what is KDD? Explain about data mining as a step in the process of knowledge discovery

 KDD stands for Knowledge Discovery in Databases, and it represents the overall process of discovering useful knowledge from large volumes of data. Data mining is a crucial step within the KDD process. Let's break down the stages of the KDD process, highlighting the role of data mining: Data Selection: This initial step involves selecting and retrieving relevant data from various sources. The data may come from databases, data warehouses, or other repositories. The goal is to gather the necessary information for analysis. Data Preprocessing: Once the data is collected, it often needs to be cleaned and preprocessed to handle missing values, outliers, and irrelevant information. This step ensures that the data is in a suitable format for analysis. Data Transformation: Data transformation involves converting the data into a suitable format for mining. This may include normalization, aggregation, or other transformations to make the data more suitable for the chosen data mining algorit...
Read more

The weights of 8 boys in kilograms: 45, 39, 53, 45, 43, 48, 50, 45. Find the median

  The weights of 8 boys in kilograms: 45, 39, 53, 45, 43, 48, 50, 45. Find the median To find the median, we need to arrange the weights in ascending order. Here are the weights of the 8 boys in kilograms: 39, 43, 45, 45, 45, 48, 50, 53 To find the median, we look for the middle value. Since there are 8 weights, the median is the average of the two middle values. In this case, the two middle values are 45 and 45. Therefore, the median weight is: (45 + 45) / 2 = 90 / 2 = 45 So, the median weight of the 8 boys is 45 kilograms. OR Here's how you can find the median of the given weights using Python: CODE:- weights = [45, 39, 53, 45, 43, 48, 50, 45] sorted_weights = sorted(weights) length = len(sorted_weights) if length % 2 == 0:     median = (sorted_weights[length // 2 - 1] + sorted_weights[length // 2]) / 2 else:     median = sorted_weights[length // 2] print("The median weight is:", median) When you run this code, it will output: The median weight is: 45.0 So, th...
Read more