Better service starts here
February 26, 2022

Better service starts here

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with aPriori

I found aPriori is a great tool that supports all stakeholders of any product lifecycle in the Design Development of Product Maintenance. Also found it as an excellent tool in the hands of a Designer to understand the impact of design on cost and identify design cost Issues and design rule violations early into the design phase. aPriori is also helping to understand the effect of alternate designs and processes.


  • Easy to understand algorithm.
  • In large databases, join and prune steps are easy to implement.
  • The resulting rules are intuitive and easy to communicate to an end user.


  • Dynamic itemset counting technique can add new candidate itemsets at any marked start point of the database during the scanning of the database.
  • The transaction reduction method reduces the number of transactions scanned in interactions. The transactions which do not contain frequent items are marked removed.
  • The sampling method picks a random sample from the database for frequent itemset. It may be possible to lose a global frequent itemset.
  • aPriori is software for frequent itemset mining and association rule learning over a relational database.
  • This software uses two steps “join” and “prune” to reduce the search space.
  • aPriori proceeds by identifying the frequent individual items in the database and extending them to larger item sets as long as those item sets appear sufficiently often in the database.
  • It improved our business model and strategies.
  • Provided get analytics for future view.
  • The main negative impact is that it is costly, a waste of time to hold a vast number of candidate sets with many frequent itemsets, low minimum support, or large itemsets.
The traditional aPriori software is evaluated in terms of support and confidence of transactional itemsets than MATLAB.

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From my experience aPriori is a classical algorithm in data mining. It is used for mining frequent itemsets and relevant association rules. Also, it is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. Whereas aPriori requires high computation if the itemsets are very large and the minimum support is kept very low and the entire database needs to be scanned.


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