Informatics Publishing Limited

Ajay Desai ¹, D. Sujatha ²

Affiliations
1 Aurora Engineering College, IN
2 CSE Department, Aurora's Technological and Research Institute, IN

Abstract

Data mining is a process of discovering knowledge of interest for the users from various information repositories like databases and data warehouses by extracting interesting data patterns that represent knowledge. These interesting data patterns are obtained by evaluating the existing data patterns based on some interestingness measures. This data mining process is carried out by using several data mining functionalities among which Association Rule Mining is most commonly used to find interesting data patterns based on the association relationship among various data items of a data set. This association relation is represented by an association rule, which is a data pattern. To evaluate the interestingness of an association rule, two interestingness measures called as support and confidence are used whose threshold values are set by domain experts. These threshold values called as minimum support and minimum confidence which represent domain knowledge. These threshold values cannot be always accurate, which leads to loss of interesting association rules and also affects the quality of association rules discovered. This leads a user to take wrong decisions.
To avoid this problem, we need to move from this support-confidence framework for a process which can find interesting association rules based on their logical correctness. An association rule is logically correct only when it satisfies a logical principle called Equivalence, such rules are called as coherent rules. As these coherent rules are discovered without using domain knowledge, no interesting association rules are lost. Though this process is accurate, it is a time consuming task to check the logical correctness of every association rule. So we need to use a technique called Pruning, through which we can get accurate results by consuming less time.

Keywords

Data Mining, Association Rules, Coherent Rule, Equivalence, Pruning.

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Shreyas Khatavkar ¹, Ajay Desai ¹, Ashish S. Mohite ¹

Affiliations
1 Department of Fisheries Engineering, College of Fisheries, Shirgaon, Ratnagiri (M.S.), IN

Abstract

Gill net is one of the popular fishing methods along the west coast of India. However, there are regional variation in the design, construction and operation. The present study deals with design, technical specification and operation of Shark gill nets operated off Satpati, Maharashtra. Shark set gill nets were made up of Polyamide (PA) multifilament of diameter 210x4x3 to 210x6x3 mm. Green and red coloured twine was commonly used for the main webbing in Satpati. The mesh size used for this gill net ranged from 90 to 100 mm for the main webbing having. The hanging co-efficient ranged between 0.39 to 0.50 and the hung length varied from 25 to 30 m and the total hung depth varied from 5.07 to 6.15 m. The fleet length of Shark gill net ranged from 2375 to 2875 m. The depth of the operation for Shark gill nets ranged from 60-90 m and were operated as bottom set gill net.

Keywords

Gill Net, Design, Shark, Shahenshah or Maghari, Scoliodon laticaudus.

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