<!DOCTYPE article
  PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "http://dtd.nlm.nih.gov/publishing/2.0/journalpublishing.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" article-type="research-article" dtd-version="2.0" xml:lang="EN">
  <front>    <journal-meta>
      <journal-title>Journal of Engineering and Technology Research</journal-title>
      <issn pub-type="epub">2006-9790</issn>      <publisher>
        <publisher-name>Academic Journals</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5897/JETR2017.0628</article-id>
      <title-group>
        <article-title><![CDATA[An improved frequency based agglomerative clustering algorithm for detecting distinct clusters on two dimensional dataset]]></article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" xlink:type="simple">
        		        	<name name-style="western">
	            <surname>Madheswaran</surname>
            <given-names>M.</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Sreedhar</surname>
            <given-names>Kumar S.</given-names>
	          </name>	
        	        </contrib>
      </contrib-group>
      <author-notes>
		<corresp id="cor1">* E-mail: <email xlink:type="simple">madheswaran.dr@gmail.com</email></corresp>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2017</year>
      </pub-date>
      <pub-date pub-type="epub">
      	<day>31</day>
        <month>12</month>
        <year>2017</year>
      </pub-date>
      <history>
      			<date date-type="received">
			<day>12</day>
			<month>07</month>
			<year>2017</year>
		</date>
						<date date-type="accepted">
			<day>11</day>
			<month>10</month>
			<year>2017</year>
		</date>
			  </history>
      <volume>9</volume>
      <issue>4</issue>
	  	  <fpage>30</fpage>
	  <lpage>41</lpage>
      <permissions>
		<license xlink:type="simple">
			<license-p>
			This is an open-access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
			</license-p>
		</license>
	  </permissions>
	  <self-uri xlink:href="http://politicalwaffle.uk/journal/JETR/article-abstract/46C8B8967119">
		This article is available from http://politicalwaffle.uk/journal/JETR/article-abstract/46C8B8967119	  </self-uri>
	  <self-uri xlink:href="http://politicalwaffle.uk/journal/JETR/article-full-text-pdf/46C8B8967119">
		The full text article is available as a PDF file from http://politicalwaffle.uk/journal/JETR/article-full-text-pdf/46C8B8967119	  </self-uri>
	  
      <abstract><![CDATA[In this study, a frequency based Dynamic Automatic Agglomerative Clustering (DAAC) is developed and presented. The DAAC scheme aims to automatically identify the appropriate number of divergent clusters over the two dimensional dataset based on count of distinct representative objects with higher intra thickness and lesser intra separation. The Distinct Representative Object Count (DROC) is introduced to automatically trace the count of distinct representative objects based on frequency of object occurrences. It also identifies the distinct number of highly comparative clusters based on the count of distinct representative objects through sequence of merging process. Experimental result shows that the DAAC is suitable for instinctively identifying the K distinct clusters over the different two dimensional datasets with higher intra thickness and lesser intra separation than existing techniques.

	 

	Key words: Dynamic automatic agglomerative clustering, clusters, intra thickness, intra separation, distinct representative object count.]]></abstract>
    </article-meta>
  </front>
      <body/>
    <back>
		<ref-list>
			<title>References</title>
						<ref id="ref1">
				<label>1</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Cadez I, Smyth P, Mannik H (2001). Probabilistic modeling of transactional data with applications to profiling, visualization and prediction, Proceedings of the Seventh ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. pp. 37-46.]]>
				</mixed-citation>
			</ref>
						<ref id="ref2">
				<label>2</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Chih-Tang C, Lai JZC, Jeng MD (2010). Fast agglomerative clustering using information of k-nearest neighbors. Pattern Recogn. 43(12):3958-3968.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref3">
				<label>3</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[De Amorim RC (2015). Feature Relevance in Wards Hierarchical Clustering Using the Lp Norm. J. Classif. 32(1):46-62.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref4">
				<label>4</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Defays D (1977). An efficient algorithm for a complete link method. Comput. J. (British Computer Society) 20(4):364-366.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref5">
				<label>5</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Douglass RC, David RK, Jan OP, John WT (1992). Scatter / Gather: A Cluster-based approach to Browsing Large Document Collections, Proceedings of the 15th annual international ACM SIGIR Conference on Research and Development in Information Retrieval pp. 318-329.]]>
				</mixed-citation>
			</ref>
						<ref id="ref6">
				<label>6</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Fionn M, Legendre P (2014). Wards hierarchical agglomerative clustering method: which algorithms implement wards criterion. J. Classif. 31(3):274-295.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref7">
				<label>7</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Fogs A, Warg W, Zaane O (2001). A non-parametric approach to web log analysis, First SAMICDM Workshop on Web Mining, Chicago pp. 41-50.]]>
				</mixed-citation>
			</ref>
						<ref id="ref8">
				<label>8</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Franti P, Kaukoranta T, Sen DF, Chang KS (2000). Fast and memory efficient implementation of the exact PNN, IEEE Trans. Image Process. 9(5):773-777.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref9">
				<label>9</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Frigui H, Krishnapuram R (1997). Clustering by competitive agglomeration, Pattern Recogn. 30(7):1109-1119.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref10">
				<label>10</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Han J, Kamber M (2006). Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA.]]>
				</mixed-citation>
			</ref>
						<ref id="ref11">
				<label>11</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Jain AK (2010). Data clustering: 50 Years beyond K-means. Pattern Recogn.Lett. 31(8):651-666.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref12">
				<label>12</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Jain AK, Murty MN, Flynn PJ (1999). Data Clustering: A Review, ACM Computer Surveys 31(3):264-323.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref13">
				<label>13</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Krishnamoorthy K, Sreedhar KS (2016). An Improved Agglomerative Clustering Algorithm for Outlier Detection. Appl. Math. Inform. Sci. 10(3):1125-1138.]]>
				</mixed-citation>
			</ref>
						<ref id="ref14">
				<label>14</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Lai JZC, Tsung-Jen H (2011). An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list. Inform. Sci. 181(9):1722-1734.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref15">
				<label>15</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Lin CR, Chen MS (2005). Combining partitional and hierarchical algorithms for robust and efficient data clustering with chesion self-merging, IEEE Trans. Knowl. Data Eng. 17(2):145-159.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref16">
				<label>16</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Pakhira KAM (2009). Modified k- means Algorithm to avoid empty clusters. Int. J. Recent Trends Eng. 1:1-8.]]>
				</mixed-citation>
			</ref>
						<ref id="ref17">
				<label>17</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Martin E, Alexander F, Hans-Peter K, Jrg S (2000). Spatial Data Mining: Database primitives, Algorithms and Efficient DBMS Support. Data Min. Knowl. Discov. 4(2-3):193-216.]]>
				</mixed-citation>
			</ref>
						<ref id="ref18">
				<label>18</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Murtagh F (1984). Complexities of Hierarchic Clustering Algorithms: the state of the art. Comput. Stat. Q. 1:101-113.]]>
				</mixed-citation>
			</ref>
						<ref id="ref19">
				<label>19</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Qi Y, Xumin L, Xiangmin Z, Andy S (2015). Efficient agglomerative hierarchical clustering. Expert Syst. Appl. 42(5):2785-2797.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref20">
				<label>20</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Sibson R (1973). SLINK: an optimally efficient algorithm for the single-link cluster method. Comput. J. (British Computer Society) 16:30-34.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref21">
				<label>21</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Wei Z, Gongxuan Z, Yongli W, Zhaomeng Z, Tao L (2015). NNB: An Efficient Nearest Neighbor Search Method for Hierarchical Clustering on Large Datasets. IEEE International Conference on Semantic Computing (ICSC). pp. 405-412.]]>
				</mixed-citation>
			</ref>
					</ref-list>
	</back>
    </article>