801 research outputs found

    Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns

    Full text link
    Understanding customer buying patterns is of great interest to the retail industry and has shown to benefit a wide variety of goals ranging from managing stocks to implementing loyalty programs. Association rule mining is a common technique for extracting correlations such as "people in the South of France buy ros\'e wine" or "customers who buy pat\'e also buy salted butter and sour bread." Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular products in the top rules. As a result, a number of "interestingness" measures (over 30) have been proposed to rank rules. However, there is no agreement on which measures are more appropriate for retail data. Moreover, since pattern mining algorithms output thousands of association rules for each product, the ability for an analyst to rely on ranking measures to identify the most interesting ones is crucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), a framework that provides analysts with the ability to compare the outcome of interestingness measures applied to buying patterns in the retail industry. We report on how we used CAPA to compare 34 measures applied to over 1,800 stores of Intermarch\'e, one of the largest food retailers in France

    New Approaches to Frequent and Incremental Frequent Pattern Mining

    Full text link
    Data Mining (DM) is a process for extracting interesting patterns from large volumes of data. It is one of the crucial steps in Knowledge Discovery in Databases (KDD). It involves various data mining methods that mainly fall into predictive and descriptive models. Descriptive models look for patterns, rules, relationships and associations within data. One of the descriptive methods is association rule analysis, which represents co-occurrence of items or events. Association rules are commonly used in market basket analysis. An association rule is in the form of X ā†’ Y and it shows that X and Y co-occur with a given level of support and conļ¬dence. Association rule mining is a common technique used in discovering interesting frequent patterns in large datasets acquired in various application domains. Having petabytes of data ļ¬nding its way into data storages in perhaps every day, made many researchers look for eļ¬ƒcient methods for analyzing these large datasets. Many algorithms have been proposed for searching for frequent patterns. The search space combinatorically explodes as the size of the source data increases. Simply using more powerful computers, or even super-computers to handle ever-increasing size of large data sets is not suļ¬ƒcient. Hence, incremental algorithms have been developed and used to improve the eļ¬ƒciency of frequent pattern mining. One of the challenges of frequent itemset mining is long running times of the algorithms. Two major costs of long running times of frequent itemset mining are due to the number of database scans and the number of candidates generated (the latter one requires memory, and the more the number of candidates there are the more memory space is needed. When the candidates do not ļ¬t in memory then page swapping will occur which will increase the running time of the algorithms). In this dissertation we propose a new implementation of Apriori algorithm, NCLAT (Near Candidate-less Apriori with Tidlists), which scans the database only once and creates candidates only for level one (1-itemsets) which is equivalent to the total number of unique items in the database. In addition, we also show the results of choice of data structures used whether they are probabilistic or not, whether the datasets are horizontal or vertical, how counting is done, whether the algorithms are computed single or parallel way. We implement, explore and devise incremental algorithm UWEP with single as well as parallel computation. We have also cleaned a minor bug in UWEP and created a more eļ¬ƒcient version UWEP2, which reduces the number of candidates created and the number of database scans. We have run all of our tests against three datasets with diļ¬€erent features for diļ¬€erent minimum support levels. We show both frequent and incremental frequent itemset mining implementation test results and comparison to each other. While there has been a lot of work done on frequent itemset mining on structured data, very little work has been done on the unstructured data. So, we have created a new hybrid pattern search algorithm, Double-Hash, which performed better for all of our test scenarios than the known pattern search algorithms. Double-Hash can potentially be used in frequent itemset mining on unstructured data in the future. We will be presenting our work and test results on this as well

    RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

    Get PDF
    In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions
    • ā€¦
    corecore