The Wild edible plants form an important Constituent of traditional diets of the tribal Community .Most of the rural populations residing in different parts of the country depend on plants and their parts to fulfill their daily needs and have developed unique knowledge about their utilization. The Present study has been conducted to document the indigenous knowledge related to the diversity and uses of wild edible weeds in day- to- day life of tribals of Sahibganj District. A total of 51 different herbs, 7 shrubs, 26 trees and 41 Climbing herbs belonging to 48 families were recorded in the present investigation out. The diversity of wild edible plants in Sahibganj district was also found to be depleting due to their over exploitation and unsustainable harvesting for foods, medicines as well as because of various other biotic interferences including grazing, herbivory and anthropogenic fire. Therefore, there is an urgent need to conserve these valuable wild edible plants and use it in a sustainable manner to ensure future demand.
As Customer relationship management (CRM) is a well established concept and its practice to enable the realization of successful Telecommunication system, data mining techniques is developed for improving the customer relationship management part mainly in Corporate Telecom Sector. Considering the existing methodology, a well established methodology with data mining is needed for development of good integrated approach with growth in time and space complexities. The aim is to find the strategic point on the essential part of Telecommunication industry by exploring the techniques of data mining. Then the focus is on presenting a new methodology in case of mobile services on the perceptions of customers of Telecommunication basing on applicability of data mining techniques to CRM databases by generating Association rules from frequent item sets on the proposed approach F-MFPG (Fast Modified Frequent Pattern Growth) by using FFIM (Fast Frequent Item sets Mining) Algorithm under concept of data mining and predicting the profit of Corporate Telecom Sector and predicting the churn for retention of customers for efficient managerial decision for reaching the ultimate goals by proposing a suitable classification techniques in data mining algorithms.
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