Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization
In recent years, the dramatic rise in the use of the internet and the improvement in technology In general have transformed societies into one that strongly depends on information and knowledge. The growth of information resources along with the accelerating rate of technological change has produ...
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| id |
my-usim-ddms-13340 |
|---|---|
| record_format |
uketd_dc |
| institution |
Universiti Sains Islam Malaysia |
| collection |
USIM Institutional Repository |
| language |
English |
| topic |
Computer algorithms Data mining Cluster analysis |
| spellingShingle |
Computer algorithms Data mining Cluster analysis Hussain Mohammad Yousef Abu Dalbouh Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization |
| description |
In recent years, the dramatic rise in the use of the internet and the improvement in
technology In general have transformed societies into one that strongly depends on
information and knowledge. The growth of information resources along with the
accelerating rate of technological change has produced massive amount of data and
information that often exceed the ability to handle and manage it. Therefore, the
demand now is creating a faster approach to handle voluminous data. This will also
improve the complexity time of the traditional hierarchical methods to face huge
collections of data and growing information flooding. In addition, user involvement in
the data mining is needed as whereby the user interact with the process through
exploitation of the power of human explanation sight and brain for analyzing and
exploring data. Clustering is an analysis technique for discovering interesting
distributions and patterns in the data set. The objects within a cluster are more similar
to each other than the objects in different clusters. This research proposed a
bidirectional agglomerative hierarchical clustering algorithm. The proposed algorithm
is fundamentally similar to conventional agglomerative hierarchical clustering
algorithms designed to partition a collection of objects into subsets sharing similar
attributes. It is obvious that analyzing large data sets via traditional methods has
moved from being tedious to being high computational cost. The traditional methods
usually not scalable to very large datasets, with an O(ri2) computational cost. However,
the proposed algorithm adapted AVL tree approach cluster the objects to left and of
right the median/root. The computational cost significantly reduced into O(Iog n). This
is efficient for huge amount of data. Thus clustering using bidirectional hierarchical
will facilitate efficient computational cost. This research demonstrated the
agglomerative algorithm performance based on complexity parameters such as
execution time and the number of cluster needed to merge all data point/objects into
one cluster. As part of the experimental validation, real data set were used to measure
the effectiveness and the efficiency of the proposed algorithm!. The study shows a
73.4% improvement from the traditional approach. The demand for visual and
interactive analysis tools is particularly pressing in this information age, where the user
needs to analyze and observe large amount of data to grasp valuable knowledge. This
research also proposed a visual cluster approach to visualize the knowledge extracted
by the data mining algorithm using AVL tree approach. The visualization prototype is
evaluated by postgraduate students who were interviewed and using Technology
Acceptance Model, as the instrument. The result revealed that visualization is useful,
easy to use and give user satisfaction. |
| format |
Thesis |
| author |
Hussain Mohammad Yousef Abu Dalbouh |
| author_facet |
Hussain Mohammad Yousef Abu Dalbouh |
| author_sort |
Hussain Mohammad Yousef Abu Dalbouh |
| title |
Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization |
| title_short |
Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization |
| title_full |
Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization |
| title_fullStr |
Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization |
| title_full_unstemmed |
Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization |
| title_sort |
algorithm development of bidirectional agglomerative hierarchical clustering using avl tree with visualization |
| granting_institution |
Universiti Sains Islam Malaysia |
| url |
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1812444858293420032 |
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my-usim-ddms-133402024-05-29T19:48:19Z Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization Hussain Mohammad Yousef Abu Dalbouh In recent years, the dramatic rise in the use of the internet and the improvement in technology In general have transformed societies into one that strongly depends on information and knowledge. The growth of information resources along with the accelerating rate of technological change has produced massive amount of data and information that often exceed the ability to handle and manage it. Therefore, the demand now is creating a faster approach to handle voluminous data. This will also improve the complexity time of the traditional hierarchical methods to face huge collections of data and growing information flooding. In addition, user involvement in the data mining is needed as whereby the user interact with the process through exploitation of the power of human explanation sight and brain for analyzing and exploring data. Clustering is an analysis technique for discovering interesting distributions and patterns in the data set. The objects within a cluster are more similar to each other than the objects in different clusters. This research proposed a bidirectional agglomerative hierarchical clustering algorithm. The proposed algorithm is fundamentally similar to conventional agglomerative hierarchical clustering algorithms designed to partition a collection of objects into subsets sharing similar attributes. It is obvious that analyzing large data sets via traditional methods has moved from being tedious to being high computational cost. The traditional methods usually not scalable to very large datasets, with an O(ri2) computational cost. However, the proposed algorithm adapted AVL tree approach cluster the objects to left and of right the median/root. The computational cost significantly reduced into O(Iog n). This is efficient for huge amount of data. Thus clustering using bidirectional hierarchical will facilitate efficient computational cost. This research demonstrated the agglomerative algorithm performance based on complexity parameters such as execution time and the number of cluster needed to merge all data point/objects into one cluster. As part of the experimental validation, real data set were used to measure the effectiveness and the efficiency of the proposed algorithm!. The study shows a 73.4% improvement from the traditional approach. The demand for visual and interactive analysis tools is particularly pressing in this information age, where the user needs to analyze and observe large amount of data to grasp valuable knowledge. This research also proposed a visual cluster approach to visualize the knowledge extracted by the data mining algorithm using AVL tree approach. The visualization prototype is evaluated by postgraduate students who were interviewed and using Technology Acceptance Model, as the instrument. The result revealed that visualization is useful, easy to use and give user satisfaction. Universiti Sains Islam Malaysia 2012-04 Thesis en https://oarep.usim.edu.my/handle/123456789/13340 https://oarep.usim.edu.my/bitstreams/1ba64555-f892-4feb-bbc4-e2e5fe124b95/download 8a4605be74aa9ea9d79846c1fba20a33 https://oarep.usim.edu.my/bitstreams/309137ea-0bcb-439d-b2be-267c744b79b0/download 0a574da3d406494da763f130f49fe553 https://oarep.usim.edu.my/bitstreams/ff6b1b05-ff07-48a1-9a2a-e71ff8cba05d/download f5ca1350e247318ac054e7defe0eebc3 https://oarep.usim.edu.my/bitstreams/186cb1cf-11ea-4e58-b9fe-d5e6c901fc33/download 0ecf716ef3f2e7f02fedfec012ff8021 https://oarep.usim.edu.my/bitstreams/bbb6b940-db61-4a14-ae64-2ddb91995df7/download bd13e2711bb537b007a58ff54d230374 https://oarep.usim.edu.my/bitstreams/04acc023-8aae-423a-8e5d-88ce13a1bed0/download 593655fc23e9b22170ac5cdb320ea55a https://oarep.usim.edu.my/bitstreams/47ba388f-f0c1-4d2c-9f0a-59209845bdd1/download e1bb1d193b906386a4b89ec702cc049c https://oarep.usim.edu.my/bitstreams/1e67a770-39c9-451f-bf5a-7d726e8919ab/download d24044a0245e4534af6e8abe1ab41e22 https://oarep.usim.edu.my/bitstreams/9a19dd80-05a7-452b-b648-49c4578a496b/download f540f396b9fc1124059363f489b7acd8 https://oarep.usim.edu.my/bitstreams/40506337-70f0-4579-8caf-c22dd32a9513/download de32ef14e43d4f52f4fafefda52a7a49 https://oarep.usim.edu.my/bitstreams/fb8fe761-d483-4d03-a673-26ffb878fdef/download ee71ca19d896db3a471475ae5f8385f1 https://oarep.usim.edu.my/bitstreams/5e401251-c0b0-44bd-8ae2-7305afe32ac4/download 0a3047d84918d3d9f86fb9de24c8d064 https://oarep.usim.edu.my/bitstreams/21756d6c-28f2-4aae-bd71-596874d6cb05/download 2c1a1929b872dc3e9bfd76d5328be222 https://oarep.usim.edu.my/bitstreams/3c3b5c4d-7229-4275-b57c-3833a4d22e75/download 11c01c83d9542474f3ca873caf2604dc https://oarep.usim.edu.my/bitstreams/eee32359-e9ee-446e-8fd1-13ef5190b651/download 64ad9066b92eb250bac8af4cbe475488 https://oarep.usim.edu.my/bitstreams/807aece3-4e56-43d0-9ced-491bd83d1566/download b24ae2117e242d5bdccb3ac6e84a2c14 https://oarep.usim.edu.my/bitstreams/5c265469-dc89-4c18-8312-54c925218680/download fd3fcfccf71282d5059d791a3efc28fa https://oarep.usim.edu.my/bitstreams/26c2f294-bef0-4f58-9204-c0a92ad1136f/download 5e37abea6bee8b2b09d68a1efa41ba2c https://oarep.usim.edu.my/bitstreams/5016df27-d53c-47bf-8eb9-8b6f242c48e9/download e3edb9ba77bc1c53beac9dbd008a628a https://oarep.usim.edu.my/bitstreams/fb07c30b-a73e-474c-a10a-f89f6e92f5a0/download 62a2d7354ec520ba5a0bc25c657007e2 https://oarep.usim.edu.my/bitstreams/31a39655-3ada-4204-8f76-5d29273214e0/download 46fac41d24c309f101d2e3ba56bb0efc https://oarep.usim.edu.my/bitstreams/2d22aefb-4aad-4257-8bd6-ae0db434f4d6/download 45e18896b2b91e88caaa127def89e2da https://oarep.usim.edu.my/bitstreams/49e2c125-e5c8-4d92-9b95-47042ef854ef/download 7ae307d0a6c4bf1d05a72af876dbdc1d Computer algorithms Data mining Cluster analysis |