Web Usage Data Clustering Using Dbscan Algorithm and Set Similarities

Santhisree, K.; Damodaram, A.; Appaji, Sangapu Venkata; Nagarjunadevi, D.

doi:10.1109/dsde.2010.14

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…Often, the purpose might be to modify the statistics in order that the hypotheses, on which the algorithms are primarily based, are confirmed, while at the identical time retaining their information content intact. One of the maximum fundamental transformation is normalization [7].…”

Section: Data Preprocessingmentioning

confidence: 99%

Discovering the behavior of the students using data mining techniques

Kamalakkannan¹,

Prasanna²

2018

IJET

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The real issue of numerous online sites is the introduction of numerous decisions for the different users at once. This normally comes about into tedious undertaking in discovering the correct item or data on the site. The user present intrigue relies on the navigational conduct which causes the associations to control users in their perusing exercises and acquire some applicable data in a limited ability to focus time. Since, the subsequent examples, which are acquired through data mining systems, did not perform well in the forecast of future temples designs due to the low coordinating rate of coming about tenets and of user's perusing conduct. This paper centers around the investigation of the pro-grammed web use data mining and proposal framework, which depends on current user conduct through his/her, snap stream information. In this paper, we attempt to show signs of improvement understanding on how Internet utilization of understudy's conduct in Engineering College can influence on their everyday scholarly exercises additionally it thinks about the use examples of various department' understudies. What's more, we endeavor to discover similitudes and dissimilarities of use examples of understudies on different branches and discovering connections between Internet utilization examples of understudies and their student performance CPI (Cumulative Performance Index). This paper displays the consequences of an investigation for a time of three months, in regards to the behavior mining of understudies identified with their Internet use designs with examining access log documents.

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Section: Data Preprocessingmentioning

confidence: 99%

Discovering the behavior of the students using data mining techniques

Kamalakkannan¹,

Prasanna²

2018

IJET

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“…The probability score of tokens and defined threshold values for cluster are the basis for the clustering process. In case of K-means [11] and DB-Scan [12], Threshold the parameters are defined initially to the algorithm for implementations. In PRUV approach, along with threshold values for cluster the probabilistic rank (Occurrence of tokens) is also provided for implementation.…”

Section: Proposed Algorithm (Pruv)mentioning

confidence: 99%

Probabilistic Ranking of Documents Using Vectors in Information Retrieval

Saini

Singh

2014

Computational Intelligence in Data Mining - Volume 1

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On the web, electronic form of information is increasing exponentially with the passage of past few years. Also, this advancement creates its own uncertainties. The overload information result is progressive while finding the relevant data with a chance of HIT or Miss Exposure. For improving this, Information Retrieval Ranking, Tokenization and Clustering techniques are suggestive as probable solutions. In this paper, Probabilistic Ranking using Vectors (PRUV) algorithm is proposed, in which tokenization and Clustering of a given documents are used to create more precisely and efficient rank gratify user's information need to execute sharply reduced search, is believed to be a part of IR. Tokenization involves pre-processing of the given documents and generates its respective tokens and then based on probability score cluster are created. Performance of some of existing clustering techniques (K-Means and DB-Scan) is compared with proposed algorithm PRUV, using various parameters, e.g. Time, Accuracy and Number of Tokens Generated.

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“…- Santhisree, Damodaram, Appaji, and NagarjunaDevi (2010), from the College of Engineering at the JNTUH University in India, presented a new method which implemented a specialized version of the DBSCAN Clustering algorithm to identify the behaviour associated with user web page visits and the order of occurrence of those visits. They used the MSNBC web navigation dataset to perform a clustering analysis to discover the groups which share common interests while using the www.…”

Section: Clusteringmentioning

confidence: 99%

A knowledge discovery approach to urban analysis

Sökmenoğlu Sohtorik

2016

Architecture and the Built Environment

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A knowledge discovery approach to urban analysis TOC 11 Contents Contents Abbreviations 21 Summary 23 Samenvatting 27 Özet 31 4.1 Conceptual Background: The city as a 'Data Mine' 95 4.2 The Model: A Knowledge Discovery Process Model (KDPM) for Urban Analysis Combining GIS and Data Mining 97 4.3 A Brief Theoretical Examination 103 4.4 Conclusion 107 TOC 13 Contents 5 Implementations of the KDPM for urban analysis in the Beyoğlu Preservation Area 109 5.1 Implementation (1) the Beyoğlu Preservation Area Building Features Database 109 5.1.1 Formulation of the Beyoğlu Preservation Area Building Features Database 113 5.1.2 Analysis and evaluation of the Beyoğlu Preservation Area Building Features Database using Data Mining 121 5.1.2.1 Test (1): Which attributes of the buildings in the Beyoğlu Preservation Area perform best in predicting the use of ground floor? 122 5.1.2.2 Test (2): What combination of building attributes in the Beyoğlu Preservation Area might provide the highest overall prediction power for the use of ground floor? 144 5.1.2.3 Test (3): Are there any associations between the use of ground floor and other building attributes in the Beyoğlu Preservation Area? 149 5.1.2.4 Test (4): Are there any recurring patterns of floorspace use within the buildings in the Beyoğlu Preservation Area? 156 5.1.3 A critical review of the implementation 168 5.2 Implementation (2) Informing the Development of Intervention Proposals for Urban Regeneration 173 5.2.1 Urban transformation policy in Istanbul 176 5.2.2 The Master Plan for the Beyoğlu Preservation Area and implementation of the Tarlabaşı renewal project 178 5.2.3 New trends in urban transformation projects; the Dutch experience 187 5.2.4 An approach to the regeneration of Tarlabaşı supported by the knowledge discovery approach to urban analysis through data mining and evolutionary computation 192 5.2.4.1 Analysis Phase 199 5.2.4.2 Synthesis Phase 204 5.2.4.3 Implementation Phase 219 5.2.5 A critical review of the implementation 242 5.3 Implementation (3) Evaluation in an International Student Workshop 249 5.3.1 Goals of the Tarlabaşı Datascope workshop 250 5.3.2 Agenda of the Tarlabaşı Datascope workshop 251 5.3.3 Work produced by the students 256 5.3.4 Students' evaluations of the Tarlabaşı Datascope workshop 268 5.3.5 A critical review of the implementation 270 5.4 Conclusion 276 TOC

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Web Usage Data Clustering Using Dbscan Algorithm and Set Similarities

Cited by 9 publications

References 3 publications

Discovering the behavior of the students using data mining techniques

Discovering the behavior of the students using data mining techniques

Probabilistic Ranking of Documents Using Vectors in Information Retrieval

A knowledge discovery approach to urban analysis

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