Kavitha V et al., International Journal of Advanced Engineering Technology E-ISSN

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1 Research Paper HIGH UTILITY ITEMSET MINING WITH INFLUENTIAL CROSS SELLING ITEMS FROM TRANSACTIONAL DATABASE Kavitha V 1, Dr.Geetha B G 2 Address for Correspondence 1.Assistant Professor(Sl.Gr), Department of Information Technology, Velalar College of Engineering and Technology, Erode, Tamilnadu, India. 2.Professor and Head, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tamilnadu, India ABSTRACT: In this shrinking modern business world making right decisions at right time is the key to success. Helping the business community to make right decision and choices, the KDD evolved itself from association rule mining and frequent patterns to the recent more complex task of mining High Utility Itemset. Many approaches have been proposed to mine HUI in recent years. But almost many of the methods focus either to reduce the number of candidate set generated or reducing the number of costly joins performed but ignores to mine the profit influencing cross selling items. Cross selling items are those items that are often bought together with a high utility item. For example, a customer who purchase a computer of any brand are likely to buy a printer of one particular brand, here the sale of computer induces the sale of printer such item that are cross sold with other items are called cross selling items. This paper is indeed focused on mining such cross selling effects in transactions. Unlike the existing algorithms this paper tries to discover some interesting patterns by taking transaction weight into consideration. KEYWORDS: High utility itemset mining, frequent pattern mining, mining based on transaction weight. 1. INTRODUCTION Affinity analysis including market basket analysis is a data analysis and mining technique that proved to be a break-through methodology for business retailers and sellers. The analysis model paved the business community to such an extent that their business decisions are based on the predicted customer buying patterns and behavior. To the simplest form Market basket analysis might give information to retailer that customers often purchase shoes and socks together, so putting both items together would create promotion at the same time would not create a significant increase in revenue, while a promotion involving just one of the items would likely increase the sales of the other item. Frequent pattern mining tries to find the item that occurs frequently in the large databases, whereas Association Rule Mining or ARM tries to find the important relationship that is present between the frequent patterns in the large database and it reveals interesting associations between items [1].Based on the associations that exists between data items, Rakesh Agrawal et al [2] introduced the association rule mining for discovering the associated items in the transaction databases. Having ARM as base various algorithms such as APRIORI[2], ECLAT[28], FP-Growth[10] etc., have been proposed. But all the algorithms considered the items in any transaction in database as equal. In contrast the items in any item set differ in many aspects. These differences strongly influence the decision making process, the values associated with each items are considered as utilities. Mining such a high utility itemset proves to be a key parameter in modern business decision making applications. Many algorithms have been developed to mine such High Utility Itemset. But all the algorithms concentrates on mining essential High Utility Itemset either through reduced joins or candidate sets and ignores to mine the profit influencing cross selling items. Cross selling items are those items are often bought together with a high utility item. For example, a customer who purchase a computer of any brand are likely to buy a printer of one particular brand, here the sale of computer induces the sale of printer such item that are cross sold with other items are called cross selling items. This paper is indeed focuses on mining such cross selling effects in transactions. Unlike the existing algorithms like FHM[8] and HUI- Miner[17] where prime importance is only on discovering high utility transactions, this paper tries to discover some interesting patterns by taking transaction weight into consideration. Some earlier HUIM algorithms such as Two-Phase, IHUP[25] and UPGrowth[25] employed methods to discover high-utility itemsets in multiple phases. These algorithms try to generate a candidate set of High-utility itemsets by extracting the utility values of the itemsets in first phase. In the following phase the algorithm ascertains the exact utility value of items in the itemsets by scanning the database and discarding the low-utility itemsets. The most profound drawback of these families of algorithms is that they generate very large number of candidate itemsets which results in vast memory consumption to store candidate itemsets and increased runtime to generate such many candidates and ascertaining their utilities, both memory and runtime remains to be most vital resource in any era of computing environment. If the subjected dataset is too huge then possibilities of memory shortage and thrashing are more prevalent. Further works on this area tried to resolve the aforesaid problem by mining HUI by generating less number of candidate itemsets. Works including IHUPTWU [25], UP-Growth and UP-Growth+ based on FP-Growth algorithm [10] showed better performance. During the first step these algorithms try to mine HUI by metamorphosing database that is mined into a prefixtree, which maintains the information about utility values of items in each itemset. In the next step a conditional prefix-tree for the item is constructed by considering the high utility itemsets containing the item provided the item is estimated to be valuable. The following step begets candidate high utility itemsets by recursively pruning the generated conditional prefix-trees. During the final step high utility itemsets are identified from the database by scanning the exact utilities of all candidates generated. Algorithms of these nature though generate less number of candidate itemsets comparatively with previous set of algorithms such

2 as Two-Phase, IHUP[25] and UPGrowth[25] yet the amount of candidates generated is high and the same drawback of memory shortage and thrashing due to higher runtime is common. In this paper observations are made on two algorithms HUI-Miner [17] and FHM [8] that tries to mine HUI without generating candidates. HUI-Miner constructs utility-list, a structure that stores utility information of an itemset together with its heuristic information that instructs whether the itemset to be pruned or not. The HUI-miner then mines HUI from these utility-lists by performing costly joins on those itemsets without generating candidate itemsets. Though it eliminates candidates, it has to perform join operation which proves to be computationally complex. FHM algorithm addresses this issue of performing join operation to generate HUI from utility list by proposing a strategy that prunes itemsets based on co-occurrence utility values. The FHM proves to be finer model comparatively with HUI-Miner. But both the algorithms missed to consider utility values of cross-selling itemsets that prove to be profit influencer in any transaction. Such items add unique weightage to each transaction in which it is contained. The remainder of this paper is organized as follows. Problem definition and related work is given in Table 1: A Quantitative Database TID TRANSACTION T 1 (I 5,1),(I 1,5)( I 2,5)(I 4,3)(I 6,5) T 2 (I 1,3)( I 5,1)( I 2,4)( I 4,3) T 3 (I 3,1)( I 1,5)( I 4,1) T 4 (I 3,6)( I 6,6)( I 1,10) Table 2: External utility Table ITEM I 1 I 2 I 3 I 4 I 5 I 6 External Utility Section 2, Section 3 describes the T- MIN algorithm, section 4 gives the experimental evaluation and section 5 is the conclusion. 2. PROBLEM DEFINITION & RELATED WORK Let I = {I 1, I 2,, I m } be a finite set of items in database D, with each item I j having corresponding external utility or profit p(i j ). An itemset X I with k distinct items of length k and is referred as a k- itemset. The transitional database is denoted as D = {T 1, T 2,, T n }, where T d D. The quantity q(i j, T d ) is the sold quantity or internal utility of item I j in transaction T d. An example database and its external utility table are given in Table 1 and Table 2 respectively. As an example of TID (T 1 ) (transaction 1) in Table 1: five quantities of item (I 1 ); five quantities of item (I 2 ); three quantities of item (I 4 ); one quantity of item (I 1 ) ; and five quantities of item (I 6 ) are purchased. The profit value p(i 1 ) of item (I 1 ) is 1 as per Table 2, which indicates that the seller gets 1 profit if one unit of item (I 1 ) is sold. Then the profit of the item (I 1 ) in transaction (T 1 ) can be expressed as p(i 1 : T 1 ), which is 5. Most of these definitions are taken from [8] and [17]. Definition 1 (Utility of an item in transaction): The utility of the single item I j in the transaction T d is given as u(i j, T d ), which can be defined as: u(i j,t d ) = quantity(i j, T d ) x profit(i j ), In which quantity(i j, T d ) is the internal utility of an itemset I j in T d, and profit(i j ) is the external utility of an itemset I j. Definition 2 (Utility of an itemset in the transaction): The utility of an itemset X in transaction T d is denoted as u(x, T d ), which can be defined as: where g(x) is the set of transactions containing X. For the given example the in Table 1, the utility of an itemset {I 1, I 2 } is calculated as u({i 1, I 2 }) = u(i 1 ) + u(i 2 ) = u(i 1,T 1 )+u(i 1,T 2 )+u(i 2,T 1 ) + u(i 2,T 2 ) = = 26 Definition 4 (Transaction utility) : The TU(Td) is the transaction utility of a transaction T d and defined as: For the given example in Table 1, the utility of (I 1 I 2 ) in TID (T 1 ) is calculated as: u(i 1 I 2, T 1 ) = u(i 1, T 1 ) + u(i 2, T 1 ) = 15 Definition 3 (Utility of an itemset in a database) : The utility of an itemset X in a database is denoted as u(x,d) and defined as `, Table 3: Transaction utilities for the running example. TID TRANSACTIONS TRANSACTION UTILITY (TU) T 1 (I 5,1),(I 1,5)( I 2,5)(I 4,3)(I 6,5) 29 T 2 (I 1,3)( I 5,1)( I 2,4)( I 4,3) 20 T 3 (I 3,1)( I 1,5)( I 4,1) 8 T 4 (I 3,6)( I 6,6)( I 1,10) 22 where m is the number of items in Td. For the given example in Table 1, the transaction utility in TID (T 1 ) is calculated as: TU(T 1 ) = u(i 5, T 1 ) + u(i 1, T 1 ) + u(i 2, T 1 ) + u(i 4, T 1 ) + u(i 6, T 1 ) = = 29 The transaction utilities of the transactions in Table 1 are shown in Table 3.

3 Definition 5 (Total utility of a database) : The total utility TU D is the sum of all TU in Database D, which can be defined as: Hence, the total utility in D is calculated as: TU D = ( ) = 79 Taking an example that runs in both Tables 1 and 3, and supposing that the minimum utility (minimutility) is set at 20. An item (I 2 ) cannot be considered as a HUI just by the reason it appears in TID (T 1, T 2 ); the utility of (I 2 ) is calculated as u(i 2 ) = u(i 2,T 1 ) + u(i 2, T 2 ) = = 18, which is less than the minim-utility count as 20. But the itemset (I 1 I 2 ) is considered as a HUI since (I 1 I 2 ) appears in TID (T 1, T 2 ) and also the utility of (I 1 I 2 ) is higher than the minimum threshold utility (minim-utility) calculated as u(i 1 I 2 ) = u(i 1 I 2, T1) + u(i 1 I 2, T2) = = 26, (26 > 20) Definition 6 (Transaction Weighted Utilization) : The TWU(X) is the transaction-weighted utilization of an X itemset and it is calculated as the addition of the transaction utility of all the transactions which contains X, that can be expressed as For the running example the TWU of item I 1 is calculated as TWU(I 1 ) = TU(T 1 ) + TU(T 2 ) + TU(T 4 ) = =79 Definition 7 (High-utility itemset, HUI): An itemset X is a high-utility itemset (HUI) in the given database D if its utility in D is not less than the user specified minim-utility count as: For the running example the TWU of item I 1 is calculated as TWU(I 1 ) = TU(T 1 )+TU(T 2 )+TU(T 3 )+ TU(T 4 ) = = 79 By analyzing the TWU values it is noted that it reflect three properties that are used in the high utility itemset pruning of the database. The properties are listed below [20]: Property #1: Hyperbolize : The TWU(X) Transaction weighted utilization of an itemset X is greater than or equal to its u(x) utility, i.e., TWU(X) u(x) Property #2: Antimonotonicity : The TWU Transaction weighted utilization of an items i is antimonotonic. Let the two itemset be X and Y and If X Y, then it can be proved that TWU(X) TWU(Y) Property #3: Pruning : Let X be an itemset in the database D. If TWU(X) < minim-utility, then the X itemset is a low-utility itemset and also all its supersets of the X itemset. Definition 8 (Utility-List) : Let is an any total order on items on all set of items I. The utility-list ul(x) of an X itemset in a database is a set of tuple such that there is a tuple with three attributes (tid; iutiles; rutiles) for each transaction T with transaction identifier T tid containing X itemset. The iutiles of the element of a tuple is the utility of X in the transaction with transaction identifier T tid. i.e u(x; T tid ). The rutiles element of a tuple is defined as For the running example the utility-list of item I 1 is calculated as U(I 1 ) = {(T 1, 5,21), (T 2, 3, 19), (T 3, 5, 8), (T 4, 10, 0)} Definition 9 (Sum of Internal Utility): Sum of Internal Utility (SIU) in a Transaction T is the total number of quantity available in a single transaction T. In our running example in Table 1 the SIU(T 1 )= =19 The HUI-Miner[17] scans the database once to create utility lists of single items to mine the high utility itemset. The second phase invokes the complex join operation to generate larger patterns. Pruning of the candidate is done in the search space using two following properties: Property #4: sum of iutiles: Let the itemset be an X. If the sum of iutiles values in the utility-list of X is less than minim-utility, then the itemset X is a lowutility itemset. Otherwise, the itemset X is a highutility itemset [12]. Property #5: sum of iutiles and rutiles. Let X is an itemset. By concatenating a y item to X itemset such that y I, for all I items in X itemset, the extensions of X be the itemset can be obtained. If the iutiles and rutiles values are summed in the utility-list of X itemset is less than minim-utility, all extensions of X and the other extensions in the total order can be left out since it is all low-utility itemset [8]. Though HUI-Miner[17] proves to be efficient algorithm in mining HUI, It shows one major drawback i.e., it performs costly join operation which reduces its smartness considerably. The FHM algorithm [8] tries to avoid this complex join operation by employing new structure EUCS (Estimated Utility Co-Occurrence Structure) to the HUI-Miner. Definition 10 (EUCS-List) : The Estimated Utility Co-Occurrence value of a item I j with item I k EUC(I j I k ) is measured as sum of TU of all transactions where I j, I k T i For the running example the Estimated Utility Co- Occurrence value of an itemset I j I k calculated as EUC(I 1 I 5 ) = TU(T 1 ) + TU(T 2 ) = =49 The EUCS structure for the running example is given in Table 4. Table 4: EUCS for the sample items of the given example ITEMSET TWU VALUES I 1 I 2 49 I 1 I 3 50 I 1 I 4 57 I 1 I 5 49

4 The FHM algorithm[8] employs the EUCS structure in form of hash map for memory efficiency. Building EUCS list is so fast and occupies less memory. After constructing EUCS, FHM performs a depth first search considering the minimum utility threshold minim-utility. The resultant is a utility list of all the items. In the further passes the FHM tries to append other items to the subject item and generate new utility list of all the items based on co occurrence and thereby reduces the number of join operation to be performed. But both the HUI-Miner and FHM never considered the cross itemsets which is eliminated as low utility itemsets. Our novel algorithm tries to yield a new set of items that proves to be profit influencer in a transaction. The algorithm mostly flows as like FHM algorithm, but in order to find the interesting cross sold itemsets that yield higher profits. The sum of all the transaction utilities in a database is normalized with the ratio of percentage with respect to an item in an itemset is calculated and recursively pruned to generate new utility values. 3. PROPOSED METHOD: Existing algorithms takes a transaction database with utility values and minimum utility (minim-utility) threshold as the input, the algorithm scans to generate transaction utility of each item. Using this transaction utility of items is compared with minim-utility to identify all possible items that are not less than minim-utility and the list is represented in a total order on items in form of ascending TWU values. During the further phase the database is rescanned to produce more exact utility value for items and from this list the database is searched to generate HUI. Our T-min algorithm takes the initial transaction database T D with utility values U(I) for each item I i in an itemset I as input and generates revised utility values RU(I) for the items in the database. During the scan the T-Min algorithm generates two temporary set of utility values for the items, (i) participation utility PU(I i ) and (ii) modified external utility MEU(I i ). Definition 11 (Participation Utility): The Participation Utility of an item is the amount the item contributes to the Transaction Utility (TU) in the single transaction. For example PU(I 1,T 4 ) = 10 x Definition 12 (Modified External Utility): The modified external utility value of an item is calculated by adding participation utility to the existing external utility of an item whenever an item participates in a transaction. Where j is the transaction in which I i participates For the running example the participation utility and modified external utility for the transaction item I 5 is shown below MEU(I 5 ) = 3+(PU(I 5, T 1 )+ PU(I 5, T 2 )) MEU(I 5 ) = 3+(2+2) =7 After the complete database scan our T-Min algorithm generates revised external utility RU Ii for each distinct item present in the transaction by a normalization process. Definition 13 (Revised Utility): The Revised Utility RU(I i ) of an item I i in an itemset of a transaction database T D is calculated as Where I* is the set of all distinct items in the database. For our running example the revised utility RU(I i ) is given in Table 5 Table 5: Revised utilities for the items of the given example ITEM Revised utility RU(I i ) I 1 3 I 2 2 I 3 1 I 4 1 I 5 1 I 6 2 The need to arrive upon such a revised utility values in the database is to ensure that the items that are initially assigned with less utility values but highly influential must be included in high utility itemsets. To understand we give a real life example of an online product rating system, where the participants can be considered as transactions and the different parameter values against the product can be considered as items. The utility value is the score assigned to the parameter of the corresponding product. In general all the algorithms that try to mine HUI concerns on individual utility values regardless of the participants. But all participants cannot be considered as same. Some participants will be active and will give the responsible feedback and some may be active for shorter period of time and inactive for the rest and some may be new to the system. All the participants cannot be treated equally; the survey report collected from the active participants needs to be considered as important. The entire process is illustrated in the following steps. Step1: Step2: Step3: Step4: Step5: Step6: Scan the database and normalize the external utilities to include the significance of items participation in a transaction Scan the database to calculate TWU for each item based on revised utility. Rearrange the items in the transaction based on the total order of TWU Build utility list of each item Build EUCS structure Construct high utility itemsets based on FHM algorithm [8] The T Min algorithm 1 takes the dataset with internal utility and set of external utility for each distinct item as the input. It scans the database D once, in that for each transaction T present in the database it calculates the Participation Utility of each item present, as given in the definition [11] and adds it to the external utility of the corresponding item. After the scan of the database, the normalization of the external utility is done as given in the definition [13], by calculating the modified external utility. All the external utilities are normalized to generate revised utility value for each item with respect to the percentage of increase in the total external utility value. This revised utility value will reflect the external utility value as well as the importance of the transaction in which the item participates. If an item

5 participates in a good transaction its revised utility value will get increased and if not vice-versa. Then the database is scanned by FHM algorithm to calculate the TWU of each distinct item using its revised utility value and a single candidate itemset is generated for which the utility value is greater than the user specified minim-utility. Candidate itemset are arranged in a total order depending on the values of TWU. The items in the transaction are also arranged in the same order. From the candidate itemset generated a hash map based EUCS is built for every two items with the corresponding TWU values. Then the recursive search procedure is called by passing an empty itemset, candidate itemset generated and the minim-utility value. If the candidate item utility value is greater than the minimutility value then it is added to the high utility itemset. Then the extensions for each itemset are passed as input and the utility values are explored by the recursive procedure. This procedure cuts down the costly join operation by the use of EUCS [Definition 10] hash map generated.it stops the pruning when the utility value of the candidate set in hash map EUCS structure is less than minim-utility thereby avoiding many costly join operation. If the extension of the itemset is greater than the minimutility value then the construct procedure is called to add the new item to the high utility itemset. The algorithm 2, 3 are reffered in [8] and 4 in [17] ALGORITHM 1: T MIN appending an item z to P, iii) minim-utility and iv) the EUCS are passed to the search procedure. Having passed on with the initial set of parameters, the search procedure investigates which extensions of P x should be searched, this is done by examining each extension Px of P. The iutiles values of the utility-list for each extension P x of P is summed up and if it is higher then minim-utility then it is considered as high-utility itemset and forwarded as output. Then the operation to merge is performed upon P x with all extensions P y of P y x to form the extensions of P xy which determines the possible extensions P y of P which must be explored, presumably if the summed up values of iutiles and rutiles in utility-list of P x is greater than minim-utility. The construct procedure [Algorithm 4] then builds the utility-list of P xy that joins the list P x and P y of P. After building the utility list structure a search procedure is recursively called with P xy is done to examine its utility and the extensions of the utility. The search procedure recursively prunes each item and appends its extensions and further prunes until all high-utility itemsets are identified. ALGORITHM 2: FHM ALGORITHM The inputs i) transaction database, ii) its utility values, iii) the minim-utility threshold are passed into FHM algorithm. The algorithm generates Transaction Weighted Utility (TWU) for each item during the first scan of database. Based on these generated TWU values the algorithm establishes the set I * that are not less than minim-utility. The shortened down itemset is then sorted in ascending order based on the TWU values. After ordering, a second scan on database is carried out during which each item in the transaction is reordered in accordance with the total order and builds utility list [definition #8] and EUCS list [definition #10] for each item i I *. The EUCS is built fast and is implemented as hash map in order to reduce the memory constraint which is well confined by I * X I *. Further, after building EUCS the algorithm calls the procedure recursively to search that takes an empty itemset, the set of single items I *, minim-utility and the EUCS structure as the input. Scan the database to calculate the TWU for each distinct items Exclude the items x which TWU (I i ) < minim-utility and construct X* Scan the database to built the utility-list of each item I i X* and build the co-occurrence EUCS structure Call Search (,I*,minim-utility, EUCS) ALGORITHM 3: SEARCH PROCEDURE The inputs i) itemset P, ii) extensions of P having the form Pz meaning that Pz was previously obtained by ALGORITHM 4 : CONSTRUCT HIGH UTILITY ITEMSETS The utility list of an itemset contains transaction id in which the item participates, the iutiles of the itemset and rutiles of the itemset. This algorithm takes (i) the utility list of itemset P, (ii) the utility list of x which is the combination of P with x, (iii) the utility list of y which is the combination of P with y where x < y in the total order of TWU of the itemset. If the construct algorithm is called for building two itemsets where the utility list of P is null then the x and y utility list can be combined together to form the xy utility list. In this the common transaction present in the utility list of x and y are identified and added in xy utility list, the iutiles of xy utility list is calculated by adding the iutiles of x and iutiles of y. In the total ordering, x<y, the rutiles of xy is the rutiles of the y. If the construct algorithm is called for itemset with more than two items where the P is not null, in this case the common transaction id of x utility list and y utility list is added as transaction id for the xy utility list, the rutiles of xy is the rutiles of y since x<y the rutiles of xy is calculated by adding iutiles of x with iutiles of y and subtracting the iutiles of P. The subtraction is done because the Px and Py are the combination of P utility itemset with x and y respectively and both Px and Py contains iutiles of P, the sum of iutiles of x and y will contain the iutile value of P twice, so the iutiles value of P is subtracted to get the correct iutiles value of xy.

6 4. EXPERIMENTAL STUDY: To evaluate the algorithm several experiments were performed on the foodmart and mushroom data sets. Foodmart is a sparse dataset and it has 1559 distinct data items in 441 transactions and the average length is 3. Foodmart dataset is sourced from Microsoft foodmart database. The internal utility and item participated in the transaction are included in the dataset. The external utility values are randomly generated between 1 to10. Mushroom is a dense dataset and it has 119 distinct items in 8124 transactions and the average transaction length is 23. Mushroom is obtained from FIMI repository; the item participated is included in the dataset. The internal utilities are generated randomly between 1 to 5 and external utility values are randomly generated between 1 to 10. The database is scanned once by T- Min algorithm. The dataset generated by the T-Min algorithm is T-foodmart and T- mushroom which have revised values for external utility respectively for foodmart and mushroom dataset. Experiments on Foodmart Dataset: Experiments were carried out by varying the minimutility count on the databases. The high utility itemset produced by both the algorithms when minim-utility is set at low the result almost remains the same. But the T-foodmart dataset includes some of the itemsets which are not in the foodmart dataset. There is a difference in the utility value of the itemset constructed. The T-Min algorithm discovers some new high utility itemsets which are not present in the earlier datasets. If an itemset participates more in the important transactions the utility count is increased and included in the new datasets. Table 6: High Utility itemset mined Foodmart dataset Results given for foodmart dataset without using T- Min Results given by T-foodmart dataset after using T- Min algorithm algorithm *994 *1413 The above Table 6 is obtained by running the FHM algorithm in the food mart dataset. It is seen that the item 994 is excluded after using T- Min algorithm and 1413 itemset is included after using the T-Min algorithm. The Table 6 is arranged in the format of high utility itemset generated by FHM algorithm. Some itemsets for example 994 in above Table 6 are missed in the High utility itemset which is present in food mart after using T-Min algorithm, though it is high utility itemset in the foodmart dataset, since because it may not have participated in the good transactions. Some itemsets for example 1413 are included in the High utility itemset which is not present in food mart after using T-Min algorithm, since because it may have participated in the good transactions. From the result shown in Figure 1 and Figure 2 it has been noted that when the minim-utility is set at low then both the dataset gives the same answer and when the minim-utility count is raised the number of high utility itemset generated and the candidate count is increased compared to the previous. Experiments on Mushroom Dataset: The experiments on the mushroom data set Figure 3 shows that many new high utility itemsets are discovered when the minim-utility value is increased. From the figure 4 the candidate set generated for the T-database is also more for higher minim-utility values. The number of candidate set generated by both the dataset has little difference but the candidate count is more when the revised utility from T-Min algorithm is used. The high utility itemset count increases if the length of the transaction is more. Fig 1: Number of Highutility itemset generated Fig 3: Number of Highutility itemset generated. Fig 2: Number of candidate count generated. Fig 4: Number of candidate count generated.

7 5. CONCLUSIONS: This model considers the cross selling effects in the transactions. It takes the quality of the transaction into the account. In retail market the customers who buy more items are important and needs to be considered. This model gives importance for the large transaction. The results shows that some low utility sets are disclosed with the cross selling effects. These itemset will be useful to identify the hidden factor and it will influence the profit. We have proposed a novel model to identify some interesting patterns which are missed by high utility itemset mining. This model can adjust the high utility itemset mined to give interesting itemsets. This model requires reasonable three or four additional database scans to find out the hidden cross selling items in the transaction. Experiment results shows that the high quality transactions are given importance. 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