Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis
Article published by Research India Publications in the International Journal of Statistics and Systems Volume 11, Number 1 (2016), pp. 19-26
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Research India Publications
2024
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| Acceso en línea: | https://www.researchgate.net/publication/306101480 https://scholar.mzumbe.ac.tz/handle/123456789/550 |
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| author | Mbukwa, Justine N. Tabita, G Neeha Anjaneyulu, GVSR Rajasekharam, OV |
| author_facet | Mbukwa, Justine N. Tabita, G Neeha Anjaneyulu, GVSR Rajasekharam, OV |
| author_sort | Mbukwa, Justine N. |
| collection | DSpace |
| description | Article published by Research India Publications in the International Journal of Statistics and Systems Volume 11, Number 1 (2016), pp. 19-26 |
| format | Article |
| id | oai:41.59.85.69:123456789-550 |
| institution | Mzumbe University |
| language | English |
| publishDate | 2024 |
| publisher | Research India Publications |
| record_format | dspace |
| spelling | oai:41.59.85.69:123456789-5502024-04-04T09:28:08Z Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis Mbukwa, Justine N. Tabita, G Neeha Anjaneyulu, GVSR Rajasekharam, OV Big data Dimensionalities Clustering analytics Article published by Research India Publications in the International Journal of Statistics and Systems Volume 11, Number 1 (2016), pp. 19-26 An interest for presenting this paper rose because of massive increase information with a very high dimensional from different sources in this era of globalization. Data are produced continuously and are unstructured (1). This paper is confined to literature review search for big data issue and challenges of several scopes in data. It brings a detailed discussion on the problem on these data and analysis done using the effective multivariate statistical tool namely clustering analysis technique as a data reduction technique. It is used as a base for discussion for existing challenge of multi-dimensionalities of data. The findings indicated that, the world is noisy due to massive flow of information continuously. Findings revealed that data emanating from face book, you tube and twitter can be used to predict the epidemic of influenza and even market trend (2 and 3). With face book data is used to predict the people`s interest. However, data from different sources have been proved to be useful in decision making efficiently and effectively for public as well as private sector. Cluster analysis technique sorts data/alike things into groups, to see if there a high natural degree association among members of the same group and low between members of different groups. Finally, this technique has proved failure to handle such heap of data with varied sources. With regards to data stored, it remains to be a challenge in terms of analysis among researchers and scientists. Therefore, it calls for advanced statistical software to cater for such an existing challenges. Private 2024-04-04T09:14:36Z 2024-04-04T09:14:36Z 2016 Article APA 0973-2675 https://www.researchgate.net/publication/306101480 https://scholar.mzumbe.ac.tz/handle/123456789/550 en application/pdf Research India Publications |
| spellingShingle | Big data Dimensionalities Clustering analytics Mbukwa, Justine N. Tabita, G Neeha Anjaneyulu, GVSR Rajasekharam, OV Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis |
| title | Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis |
| title_full | Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis |
| title_fullStr | Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis |
| title_full_unstemmed | Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis |
| title_short | Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis |
| title_sort | statistical analytic tool for dimensionalities in big data the role of hierarchical cluster analysis |
| topic | Big data Dimensionalities Clustering analytics |
| url | https://www.researchgate.net/publication/306101480 https://scholar.mzumbe.ac.tz/handle/123456789/550 |
| work_keys_str_mv | AT mbukwajustinen statisticalanalytictoolfordimensionalitiesinbigdatatheroleofhierarchicalclusteranalysis AT tabitagneeha statisticalanalytictoolfordimensionalitiesinbigdatatheroleofhierarchicalclusteranalysis AT anjaneyulugvsr statisticalanalytictoolfordimensionalitiesinbigdatatheroleofhierarchicalclusteranalysis AT rajasekharamov statisticalanalytictoolfordimensionalitiesinbigdatatheroleofhierarchicalclusteranalysis |