Informatics Publishing Limited

Navneet ¹, Nasib Singh Gill ¹

Affiliations
1 Department of Computer Science and Applications, MDU, Rohtak - 124001, Haryana, IN

Abstract

Objectives: To optimize data mining technique for big data. Methods/Analysis: This paper designs a technique for the data mining of big data by modifying the existing data mining technique using fuzzy and neural network. The present technique firstly performs the dimension reduction. Then reduced dimension datasets are clustered, while the remaining attributes are used to classify such dataset by using automated fuzzy. Findings: The existing data mining techniques are not optimized on such data. The simulation using the fuzzy on various dataset shows the optimization of technique. The RNFCA algorithm is analyzed adding the RNFCA algorithm to the WEKA library on the Intel i5 @ 2.67 GHz using the eclipse IDE. The algorithm is analyzed on the datasets having 400 instances with 25 attributes and 32561 instances with 15 attributes. The detail description of these datasets is given in table 2. The performance of the RNFCA algorithm can be compared with existing CCSA algorithm and the decision tree i.e. J48. The figure 4 -7 shows the comparison graph of the J48, CCSA and RNFCA over various parameters. Applications/Improvement: The simulation using the fuzzy on various dataset shows the optimization of technique.

Keywords

BIG Data, Dimension Reduction, Fuzzy, K-Mean, Neural, Schwarz Criteria.

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Navneet ¹, Nasib Singh Gill ¹

Affiliations
1 Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, IN

Abstract

Various attributes within a dataset relate to each other and with the class attribute. The relationship between the different attributes with class attribute may improve the classification accuracy. The paper introduces CCSA algorithm that performs the clustering that is cascaded by classification based on association. The Clustering process generates a group of various instances within the dataset. These clustered instances are classified by using the association. This paper uses the Apriori association to generate the rules for classification. The technique is analyzed by using the soil data set and various other online available datasets using WEKA. The simulation result using the WEKA shows that reduced rules with the improved classification accuracy as compared to the existing association with classification algorithms.

Keywords

Data Mining, PART, WEKA, k-Mean Clustering, Schwarz Criteria, Association.

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Akash ¹, Navneet ¹, B. S. Bhandari ²

Affiliations
1 Gurukul Kangri University, Haridwar 249 401, IN
2 H.N.B. Garhwal University, Srinagar, Uttarakhand 246 174, IN

Abstract

The present study was conducted to examine the regeneration pattern in 72 random plots of six tropical forest sites of Rajaji Tiger Reserve, Uttarakhand, India. The population structure of the forest was determined through density of seedlings, saplings and trees from the sampling quadrat. Based on individual density of seedling, sapling and adult, the regeneration potential of the species was determined. A total of 58 tree species under 50 genera belonging to 30 families were recorded in the forest. The overall density ranged from 1525 to 6600 ind/ha and the total basal area ranged from 0.577 to 46.81 m²/ha in case of trees and saplings, whereas for seedlings, the value ranged from 511.96 to 1221 cm²/ha. The good regeneration pattern of tree species varied from 12.12% to 31.575%, fair regeneration pattern from 12.12% to 31.57%, new regeneration from 5.26% to 39.13%, poor regeneration from 0% to 10.52%, and no regeneration of trees from 15.78% to 42.42%. Inadequate regeneration status and population structure of tree species like Shorea robusta, Careya arborea, Ficus auriculata were observed which could be due to looping, scraping, grazing and trampling. Anthropogenic disturbances have resulted in the population decline of tree species which may lead to many species becoming endangered, rare and threatened. Therefore, proper management and conservation initiatives with active involvement of the locals must be taken to protect the tropical forest sites of the Reserve.

Keywords

Population Structure, Natural Regeneration, Saplings, Seedlings, Tiger Reserve, Trees, Tropical Forest.

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