In data mining, the extraction of frequent patterns from large databases is still a challenging and difficult task due to the various drawbacks such as, high response time, communication cost to alleviates such issues, a new algorithm namely single scan distributed pattern mining algorithm (SSDPMA) is proposed in this paper for frequent mining. The frequent patterns are extracted in a single scan of the database. Then, it is split into multiple files, which will be shared to multiple virtual machines (VMs) to store and compute the weight for the distinct records. Then, the support, confidence and threshold values are estimated. If the limit is greater than the given data, the frequent data are mined by using the proposed SSDPMA algorithm. The experimental results evaluate the performance of the proposed system in terms of response time, message size, execution time, run time and memory usage.
In data mining, the extraction of frequent patterns from large databases is still a challenging and difficult task due to the various drawbacks such as, high response time, communication cost to alleviates such issues, a new algorithm namely single scan distributed pattern mining algorithm (SSDPMA) is proposed in this paper for frequent mining. The frequent patterns are extracted in a single scan of the database. Then, it is split into multiple files, which will be shared to multiple virtual machines (VMs) to store and compute the weight for the distinct records. Then, the support, confidence and threshold values are estimated. If the limit is greater than the given data, the frequent data are mined by using the proposed SSDPMA algorithm. The experimental results evaluate the performance of the proposed system in terms of response time, message size, execution time, run time and memory usage.
Data mining environment gives a quick response to the user by fast and correctly pick-out the item from the large database is a very challenging task. Previously, multiple algorithms were proposed to identify the frequent item since they are scanning database at multiple times. To overcome those problems, we proposed Rehashing based Apriori Technique in which hashing technology is used to store the data in horizontal and vertical formats. Rehash Based Apriori uses hashing function to reduce the size of candidate item set and scanning of database, eliminate non-frequent items and avoid hash collision. After finding frequent item sets, perform level wise subspace clustering. We instigate generalised self organised tree based (GSTB) mechanism to adaptively selecting root to construct the path from the cluster head to neighbours when constructing the tree. Our experimental results show that our proposed mechanisms reduce the computational time of overall process.
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