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Search Results: 1 - 10 of 332056 matches for " K. R. Pardasani "
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Efficient Method for Multiple-Level Association Rules in Large Databases
Pratima Gautam,K. R. Pardasani
Journal of Emerging Trends in Computing and Information Sciences , 2011,
Abstract: The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this paper, we present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. Even in computing, for the number of occurrence of an item, we require to scan the given database a lot of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper, a new approach is introduced for solving the abovementioned issues. Therefore, this algorithm above all fit for very large size databases. We also use a top-down progressive deepening method, developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle
A Fast Algorithm for Mining Multilevel Association Rule Based on Boolean Matrix
Pratima Gautam,K. R. Pardasani
International Journal on Computer Science and Engineering , 2010,
Abstract: In this paper an algorithm is proposed formining multilevel association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithmfirst time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method.
Rough Set Model for Discovering Hybrid Association Rules
Anjana Pandey,K. R. Pardasani
Computer Science , 2009,
Abstract: In this paper, the mining of hybrid association rules with rough set approach is investigated as the algorithm RSHAR.The RSHAR algorithm is constituted of two steps mainly. At first, to join the participant tables into a general table to generate the rules which is expressing the relationship between two or more domains that belong to several different tables in a database. Then we apply the mapping code on selected dimension, which can be added directly into the information system as one certain attribute. To find the association rules, frequent itemsets are generated in second step where candidate itemsets are generated through equivalence classes and also transforming the mapping code in to real dimensions. The searching method for candidate itemset is similar to apriori algorithm. The analysis of the performance of algorithm has been carried out.
A Novel Approach For Discovery Multi Level Fuzzy Association Rule Mining
Pratima Gautam,K. R. Pardasani
Computer Science , 2010,
Abstract: Finding multilevel association rules in transaction databases is most commonly seen in is widely used in data mining. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed fuzzy set concepts, multi-level taxonomy and different minimum supports to find fuzzy multilevel association rules in a given transaction data set. Apriori property is used in model to prune the item sets. The proposed model adopts a topdown progressively deepening approach to derive large itemsets. This approach incorporates fuzzy boundaries instead of sharp boundary intervals. An example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.
Finite Element Model to Study Two Dimensional Unsteady State Cytosolic Calcium Diffusion in Presence of Excess Buffers
Shivendra G. Tewari,K. R. Pardasani
IAENG International Journal of Applied Mathematics , 2010,
Abstract:
A Model to Study Effect of Rapid Buffers and Na+ on Ca2+ Oscillations in Neuron Cell
Vikas Tewari,Shivendra Tewari,K. R. Pardasani
Journal of Mathematics Research , 2010, DOI: 10.5539/jmr.v2n1p74
Abstract: $Ca^{2+}$ plays a vital role in muscle mechanics, cardiac electrophysiology, secretion, hair cells, and adaptation in photoreceptors. It is a vital second messenger used in signal transduction. Calcium controls cell movement, cell differentiation, ciliary beating. Many cells exhibit oscillations in intracellular [Ca$^{2+}$] in response to agonist such as hormones and neurotransmitters. Many cells use oscillations in calcium concentration to transmit messages (Sneyd J. et al, 2006, p. 151-163). In this paper, an attempt has been made to develop a model to study calcium oscillations in neuron cells. This model incorporates the effect of variable Na$^{+}$ influx, sodium-calcium exchange (NCX) protein, Sarcolemmal Calcium ATPase (SL) pump, Sarco-Endoplasmic Reticulum CaATPase (SERCA) pump, sodium and calcium channels, and $IP_{3}R$ channel. The proposed mathematical model leads to a system of partial differential equations which has been solved numerically using Forward Time Centered Space (FTCS) approach. The numerical results have been used to study the relationships among different types of parameters such as buffer concentration, disassociation rate, calcium permeability, etc.
A Rough Sets Partitioning Model for Mining Sequential Patterns with Time Constraint
Jigyasa Bisaria,Namita Shrivastava,K. R. Pardasani
Computer Science , 2009,
Abstract: Now a days, data mining and knowledge discovery methods are applied to a variety of enterprise and engineering disciplines to uncover interesting patterns from databases. The study of Sequential patterns is an important data mining problem due to its wide applications to real world time dependent databases. Sequential patterns are inter-event patterns ordered over a time-period associated with specific objects under study. Analysis and discovery of frequent sequential patterns over a predetermined time-period are interesting data mining results, and can aid in decision support in many enterprise applications. The problem of sequential pattern mining poses computational challenges as a long frequent sequence contains enormous number of frequent subsequences. Also useful results depend on the right choice of event window. In this paper, we have studied the problem of sequential pattern mining through two perspectives, one the computational aspect of the problem and the other is incorporation and adjustability of time constraint. We have used Indiscernibility relation from theory of rough sets to partition the search space of sequential patterns and have proposed a novel algorithm that allows previsualization of patterns and allows adjustment of time constraint prior to execution of mining task. The algorithm Rough Set Partitioning is at least ten times faster than the naive time constraint based sequential pattern mining algorithm GSP. Besides this an additional knowledge of time interval of sequential patterns is also determined with the method.
A Model for Mining Multilevel Fuzzy Association Rule in Database
Pratima Gautam,Neelu Khare,K. R. Pardasani
Computer Science , 2010,
Abstract: The problem of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging, when some form of uncertainty like fuzziness is present in data or relationships in data. This paper proposes a multilevel fuzzy association rule mining models for extracting knowledge implicit in transactions database with different support at each level. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. This approach incorporates fuzzy boundaries instead of sharp boundary intervals. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.
An Algorithm for Mining Multidimensional Fuzzy Association Rules
Neelu Khare,Neeru Adlakha,K. R. Pardasani
Computer Science , 2009,
Abstract: Multidimensional association rule mining searches for interesting relationship among the values from different dimensions or attributes in a relational database. In this method the correlation is among set of dimensions i.e., the items forming a rule come from different dimensions. Therefore each dimension should be partitioned at the fuzzy set level. This paper proposes a new algorithm for generating multidimensional association rules by utilizing fuzzy sets. A database consisting of fuzzy transactions, the Apriory property is employed to prune the useless candidates, itemsets.
FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases
Virendra Kumar Shrivastava,Dr. Parveen Kumar,Dr. K. R. Pardasani
International Journal of Computer Science and Information Security , 2010,
Abstract: In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.
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