Contexts in source publication

Context 1
... of Itemset (U) = internal utility (i) *external utility (e) Example Let Table 1 be a database containing five transactions. Each row in Table 1 represents a transaction, in which each letter represents an item and has a purchase quantity (internal utility). ...
Context 2
... of Itemset (U) = internal utility (i) *external utility (e) Example Let Table 1 be a database containing five transactions. Each row in Table 1 represents a transaction, in which each letter represents an item and has a purchase quantity (internal utility). Table 2 represents the unit profits associated with each itemset. ...
Context 3
... of Itemset (U) = internal utility (i) *external utility (e) Example Let Table 1 be a database containing five transactions. Each row in Table 1 represents a transaction, in which each letter represents an item and has a purchase quantity (internal utility). ...
Context 4
... of Itemset (U) = internal utility (i) *external utility (e) Example Let Table 1 be a database containing five transactions. Each row in Table 1 represents a transaction, in which each letter represents an item and has a purchase quantity (internal utility). Table 2 represents the unit profits associated with each itemset. ...

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Citations

... These processed data are uncertain, and how to discover them correctly and effectively is a hot field of research. Ninoria et al. [28] proposed the HURIU algorithm for mining HURIs over uncertain database. This innovative approach uses the concept of Aprioriinverse over uncertain databases and gives a new extension of the HURI algorithm. ...
... This section will address the proposed innovative approach for mining high utility Rare itemsets over uncertain databases using apriori-inverse [2],Improved Apriori [1] and Improved UHUI-Apriori [53] using idea of improved apriori algorithm .The major limitations in apriori algorithm has been focussed and updated the process of item generation method. Extensive experiments have been performed to test the performance of these approaches over our sample example dataset. ...
... The same thing for C3, construct 3-itemset C(x, y, z), where x, y and z are the items of C3 and use L1 to get the transaction IDs,with their minimum support count between x, y and z, then scan for C3 only in these specific transactions and repeat these steps until no new frequent itemsets are identified. The process can be clearer through Figure1 given below: Figure 1: Steps for Ck Generation [1] On the basis of the approach proposed in [22], shalini et al has proposed a novel efficient algorithm HURIU which reduced the extraction time of high utility rare itemsets over uncertain database drastically [53].The explanation of the working process of complete phases have also been explained with the help of experiment on a sample dataset. The proposed approach is a two phase method. ...
... For the experimental assessment consider the sample uncertain database is given in Table 1 and Table 2 where each transaction is assigned a unique probability value in the range of 0.1 to 0.5.The experiment have performed for different higher and lower threshold values viz 25%, 35%, 40%, 50%, and 60% respectively. This implementation has done for the performance analysis of the proposed algorithm IHURIU,HURIU [53],UHUI-apriori [3].The performance has been evaluated on the basis of three parameters i) Run Time ii) Memory Utilization iii) Total Number of Itemsets Generated. ...
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In present era, as there are many applications of uncertain data hence more emphasis has paying focus on mining itemsets over uncertain databases. Data mining is a technique which is useful in the extraction of interesting relationships between data in huge databases. Association rule mining is one of the most vital techniques of data mining in which association among the items present in the transactions are discovered. High-utility itemset mining (HUIM) has come up as a most significant research topic in data mining. High utility rare itemsets in a database can be used by retail stores to adapt their marketing strategies in order to increase their profits. Even though the itemsets mined are infrequent, since they generate a high profit for the store, marketing strategies can be used to increase the sales of these items. In this paper, a novel approach named A Hybrid Approach for Mining High Utility Rare Itemsts over Uncertain Database ((Improved High Utility Rare Itemset over Uncertain Database (IHURIU) algorithm) is proposed to generate all high utility rare itemsets while keeping the algorithm time-efficient as well as memory-efficient. This proficient approach improved the concept of apriori inverse over uncertain database and it will give blend of Improved Apriori[1],apriori-inverse[2] and UHUI-apriori [3] algorithm approaches in the form of hybrid proposed approach. This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al as it will give the improvement in the basic apriori algorithm for item generation as well as implementation of HURI over uncertain database. The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[3] for uncertain database.