25 research outputs found
An Approach to Text Documents Clustering with {n, n-1, ….., 1}-Word(s) Appearance Using Graph Mining Techniques
Get PDFThis paper is about text document clustering with an input of n words. Initially a cluster of all text documents with extension name ".Txt" from m-documents of various types is formed. Then on an input of n-words, the proposed algorithm starts n, n-1, n-2,.....,1 sets of cluster. Each cluster of text documents with the presence of n, n-1, n-2,......,1 word(s) respectively. These n-forms of clustering are treated as documents-words relation and in memory it is represented as un-oriented documents-words incidence matrix. Finally these un-oriented documents-words incidence matrices are represented as bi-partite graphs, since the bi-partite graph has two sets of nodes namely document and word. The proposed algorithm using graph mining techniques was implemented using C++ programming language and the result was satisfactory
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No full textNot AvailableOften geographical boundaries of the climatic zones identified differ from the administrative boundaries. Eventually planners and administrators are unable to use these classifications while formulating new developmental programmes. Though few studies attempted to bring the climatic classification to district level in the past, the climatic datasets used in such studies were found to be relatively old. Climate change literature pertaining to India showed evidence of rising mean temperatures during post-1970 period. The temperature rise affects potential evapotranspiration and consequently the aridity is expected to increase at least at macro level though there may be spatial variation at a smaller geographical scale. In the present study, an attempt has been made to assess the climate at district level using latest data and examine climatic shift occurred, if any, as compared to the climatic classification given by Krishnan in 1988. The study used 0.5 degree X 0.5 degree grid level rainfall data and average potential evapotranspiration for 144 stations located across India to compute moisture index needed for delineation of different climatic zones. Both datasets refer to the period 1971–2005. Significant reflections resulting from the study indicated a substantial increase of arid region in Gujarat and, a decrease of arid region in Haryana. Other notable observations included the increase in semi-arid region in Madhya Pradesh, Tamil Nadu and Uttar Pradesh due to shift of climate from dry sub-humid to semi-arid. Likewise, the moist sub-humid pockets in Chhattisgarh, Orissa, Jharkhand, Madhya Pradesh and Maharashtra states have turned dry sub-humid to a larger extent. Updated climatic classification of this sort at district level shall be useful to various stakeholders for agricultural planning, assessment of water demand by different sectors, drought preparedness, assessment of climate driven pests/diseases in humans, crops and livestock, etc.Not Availabl
