Claims:1. This invention provides an algorithm approach to find and predict the high [0075] utility item set from the geographical databases which consists of sets of transactions

2. According to Claims 1 # the invention is to capture the geographical location-based transactions which stores at cloud server.
3. According to Claims 1 # the invention is the high utility item by sending the request to cloud based on the geographical.
4. According to Claims 1 # the invention is to receive or locate the geographical transactions base on the request.
5. According to Claims 1 # the invention is to Transactions data will be transmitting in JSON (JavaScript Object Notation) format from client to cloud server.
5. According to Claims 1 # the invention is to Data synchronization between Cloud Server and ERP System using REST API with JSON format.
6. According to Claims 1 # the invention is to Algorithm will search the HUIM among the transactions with a smaller number of iterations (candidate search) due to geographical location.
7. According to Claims 1 # the invention is to with the invention, a company/academic institution/factory/etc. can predict the HUIM and geographical. This helps decision driven solution speedy
, Description:1. When series of transactions, it is tedious to classification on geo-points and finding the high utility item set.
2. Processing all transactions will leads to degrading the performance.
3. A high utility item set is a set of values that appears in a database and has a high importance to the user, as measured by a utility function. This utility will [0040]take more number of search and leads to less performance
[0045]As a result of the above-mentioned drawbacks and the deficiencies of present solutions relating the subject, a new innovative improvement has to be made in the related technical field.
Existing Technologies: Identification of HUIM in sign on database, reading data in sequential pattern, constant supportive measure (frequency) / threshold value which is specified by user.
[0050]Drawbacks: More search space, less in performance, and more internal memory. Delay in Data Synchronization to the ERP systems, etc.
1. The problem of high utility item set mining is di?cult for two main reasons. The ?rst reason is that the number of item sets to be considered can be very large to ?nd those that have a high utility. If there are ‘n’ distinct data sets, then there will 2*n -1 searches required.
2. A naive approach to solve the problem of high utility item set mining is to count the utilities of all possible itemsets by scanning the database, to then keep the high utility itemsets. Although this approach produces the correct result, it is ine?cient. The reason is that the number of possible itemsets can be very large. For example, if a retail store has 10,000 items on its shelves (m = 10,000), the utilities of 210,000 -1 possible itemsets should be calculated, which is unmanaged able using the naïve approach.
3. the size of the search space does not only depend on the size of the database, but also on how similar the transactions are in the database, how large the utility values are, and also on how low the minutia threshold is set by the user.
4. The ?rst one is that because Two-Phase generates itemsets by combining itemsets without looking at the database, it can generate some patterns that do not even appear in the database. Thus, Two-Phase can spend a large amount of time processing itemsets that do not exist in the database. The second limitation is that Two-Phase repeatedly scans the database to calculate the TWU and utilities of itemsets, which is very costly. The third limitation is that using a breadth-?rst search can be quite costly in terms of memory as it requires at any moment to keep in the worst case all k-itemsets and (k -1)-itemsets in memory (for k > 1).
[0055]What problem do the inventions address and how is your invention able to overcome the limitations/ problems of existing technologies?

5. Proposed Invention address the below problems:
• By applying the classification of itemsets with quantity values or databases.
• Suppose among all items in a store, classify and divide the most unit value data base, second most unit value database…etc.
• Then based on the customer transactions, refer the unit database when required instead of for every search.
• Efficiency & Improve the productivity.
• Cost Effective (Low Cost).
• Data Integration and Synchronization.