Uncertain Big Data Strategical Miner - Sapte, HV; Pallati, SS; Pandit, PP; Joshi, AS; Jumb, V (International Journal of Computer Sciences and Engineering, 2025)
There are many data mining algorithms which exist today for searching patterns from transactional databases. Most of them work only on precise data. But there are also situations in which these conventional algorithms fail, situations in which Data is uncertain in nature. Uncertain data can be explained as the one where items have probabilistic values associated with them. These probabilities express the likelihood of these items to be present in the transactions. In mining, the search tree produced is also one of the major factor of concern. The search space produced when dealing with uncertain data is much larger due to the presence of existential probabilities. This problem worsens when dealing with Big data. Considering all the above factors and concerns, an algorithm is specified and explained ahead. It allows users to express the interest in terms of constraints and uses the Map Reduce programming model to mine uncertain Big data for frequent patterns that satisfy the user-specified constraints. By using these user-specified constraints as inputs, the algorithm greatly reduces the search space for Big data mining of uncertain data, and returns only those patterns the users are interested in.