Background: Banana (Musa Paradisicia) is considered as one of the most important fruit crop across the globe. India is the largest producer of banana in the world with an output of 29 million tons per year on average. Assam, a state in the north eastern region of India, is one of the major banana producing state in the country. Banana is an important horticulture crop grown in Assam with some significant socio cultural importance. The production of banana in Assam accounts for 2.4 per cent of its total production in the country.Methods: The paper is an attempt to study the rate of growth and instability of area, production and productivity of banana cultivation in Assam and to compute the relative contribution of area, productivity and their interaction to the change in production of banana in the state. The study is based on secondary data for the period of 2003-04 to 2017-18. Compound annual growth rates are computed for estimating the growth trends of area, production and productivity of banana in Assam whereas Cuddy-Della Valle index of instability is used to estimate the instability.Result: The results of this study revealed positive growth in area, production and productivity of banana with low instability in the state. The productivity effect has been found marginally greater than the area effect on production of banana in the state during the reference period.

This paper presents CSGM 2 , a text preprocessing technique for compression purposes. It converts the original text into a word net (graph representation) and can retain the detailed contextual information such as word proximity. Specific directed graph is proposed to model this word net where words are stored in vertices and edges represent word transitions. The word net is fully capable of holding the natural word order in the original text and hence can be used directly for encoding purposes.

Objectives:The objectives of this research article is to deal with the problem of web document clustering by modeling the web documents as directed completely labeled graphs that incorporate contextual information in the computation process to the extent required. The computational complexity of the MCS algorithm based on this graph model is O(n 2 ), n being the number of nodes. As graph similarity using MCS is an NP-complete problem, so this is an important result that allows us to forgo sub-optimal approximation approaches and find the exact solution in polynomial time. Method: The first step towards this new approach of web document clustering is the representation of the web documents with the help of a directed completely labeled graph that can retain contextual information of the document under consideration. After graphical modeling of the document, the next step is the calculation of similarity between the graphical objects. For this purpose, a customized algorithm proposed as Algorithm for Maximum Common Subgraph Isomorphism (AMCSI) (1) based on a backtracking search scheme is being used. The proposed AMCSI algorithm is solving the problem of maximum common subgraph isomorphism in polynomial time. After obtaining the value for the similarity between the graphical objects we are again using a customized fuzzy-c means algorithm to produce clusters from the target set of web documents. We are using multidimensional scaling to express the distance values between the web pages (graphs) in two coordinates (x,y) and deterministic sampling to calculate the graph median in the process of fuzzy c-means clustering. Findings: We present an alternative method for web document clustering by representing the web documents as directed completely labeled graphs where the computational complexity of the MCS algorithm is O(n 2 ) (1) . A new distance measure is also developed based on the directed completely labeled graph representation which is giving 16.9% better result than the prevailing methods (2) . For the clustering purpose, we have chosen the fuzzy cmeans clustering algorithm and customizing the original algorithm to fit with graphical objects. This approach enables us to model the web documents as graphs without discarding contextual information and then cluster these graphical objects with the help of a well-established clustering algorithm.