APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING
Clustering data or grouping data is an important tool for a variety of applications. The most popular class of clustering is Kmeans algorithm. However, this algorithm not ensures to get the global optimum when doing the clustering. That because Kmeans algorithm highly depends on the initial states a...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/24862 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| id |
id-itb.:24862 |
|---|---|
| spelling |
id-itb.:248622017-09-27T11:43:14ZAPPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING RAMADHANINGRUM (10112079), ZUHAIRINA Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/24862 Clustering data or grouping data is an important tool for a variety of applications. The most popular class of clustering is Kmeans algorithm. However, this algorithm not ensures to get the global optimum when doing the clustering. That because Kmeans algorithm highly depends on the initial states and trapped to the local minimum, and gets a bad results when the data has an outliers. Thus, the algorithm needs to upgraded to prevent that things happened. One of the algorithm that can be used is artificial bee colony algorithm. Artificial bee colony algorithm is one of <br /> <br /> <br /> metaheuristic method, a method which guides a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space to find <br /> <br /> <br /> global near-optimal solution. text |
| institution |
Institut Teknologi Bandung |
| building |
Institut Teknologi Bandung Library |
| continent |
Asia |
| country |
Indonesia Indonesia |
| content_provider |
Institut Teknologi Bandung |
| collection |
Digital ITB |
| language |
Indonesia |
| description |
Clustering data or grouping data is an important tool for a variety of applications. The most popular class of clustering is Kmeans algorithm. However, this algorithm not ensures to get the global optimum when doing the clustering. That because Kmeans algorithm highly depends on the initial states and trapped to the local minimum, and gets a bad results when the data has an outliers. Thus, the algorithm needs to upgraded to prevent that things happened. One of the algorithm that can be used is artificial bee colony algorithm. Artificial bee colony algorithm is one of <br />
<br />
<br />
metaheuristic method, a method which guides a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space to find <br />
<br />
<br />
global near-optimal solution. |
| format |
Final Project |
| author |
RAMADHANINGRUM (10112079), ZUHAIRINA |
| spellingShingle |
RAMADHANINGRUM (10112079), ZUHAIRINA APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING |
| author_facet |
RAMADHANINGRUM (10112079), ZUHAIRINA |
| author_sort |
RAMADHANINGRUM (10112079), ZUHAIRINA |
| title |
APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING |
| title_short |
APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING |
| title_full |
APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING |
| title_fullStr |
APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING |
| title_full_unstemmed |
APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM IN DATA CLUSTERING |
| title_sort |
application of artificial bee colony algorithm in data clustering |
| url |
https://digilib.itb.ac.id/gdl/view/24862 |
| _version_ |
1822921366549037056 |