QUOTATION SERVER AND CLIENT

-


A simple program of server and client where the server sent a good quote to the connected client immediately after the connection(as welcoming).
Multiple Clients are handled using java multithreading concept, Used UDP datagram sockets, and text where quotes are stored. 

The output of the above program:


Outputs after quotes are completed:

Comments

Post a Comment

Popular posts from this blog

Splitting criteria

-
Image
This package helps to find splitting criteria in C4.5 Information Gain ID3 uses information gain as its attribute selection measure. This measure is based on pioneering work by Claude Shannon on information theory, which studied the value or “information content” of messages. Let node N represent or hold the tuples of partition D. The attribute with the highest information gain is chosen as the splitting attribute for node N. This attribute minimizes the information needed to classify the tuples in the resulting partitions and reflects the least randomness or “impurity” in these partitions. Such an approach minimizes the expected number of tests needed to classify a given tuple and guarantees that a simple (but not necessarily the simplest) tree is found. The expected information needed to classify a tuple in D is given by: where pi is the probability that an arbitrary tuple inDbelongs to classCi and is estimated by |Ci,D|/|D|. A log function to the base 2 is used becaus
Read more

How to Handle Noisy Data in preprocessing of data?

-
Binning method:(one of the method) first sort data and partition into (equi-depth) bins then one can smooth by bin means,  smooth by bin median, smooth by bin boundaries, etc. Equal-width (distance) partitioning: It divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B-A)/N. The most straightforward But outliers may dominate the presentation Skewed data is not handled well. Equal-depth (frequency) partitioning: It divides the range into N intervals, each containing approximately same number of samples Good data scaling Managing categorical attributes can be tricky. Code for binning (if needed we can edit for user input instead of random) : Feel free to comment about mistakes and doubts.
Read more