A New Measure of Complementarity in Market Basket Data

Calculating a New Data Mining Algorithm for Market Basket Analysis

The general goal of data mining is to extract interesting correlated information from large collection of data. A key computationallyintensive subproblem of data mining involves nding frequent sets in order to help mine association rules for market basket analysis. Given a bag of sets and a probability, the frequent set problem is to determine which subsets occur in the bag with some minimum pr...

a study on insurer solvency by panel data model: the case of iranian insurance market

the aim of this thesis is an approach for assessing insurer’s solvency for iranian insurance companies. we use of economic data with both time series and cross-sectional variation, thus by using the panel data model will survey the insurer solvency.

Finding Localized Associations in Market Basket Data

In this paper, we discuss a technique for discovering localized associations in segments of the data using clustering. Often the aggregate behavior of a data set may be very di erent from localized segments. In such cases, it is desirable to design algorithms which are e ective in discovering localized associations, because they expose a customer pattern which is more speci c than the aggregate...

Customer Visit Segmentation Using Market Basket Data

A New Efficient Method for Mining Frequent Itemsets in Market Basket Data Analysis

Discovery of hidden and valuable knowledge from large data warehouses is an important research area and has attracted the attention of many researchers in recent years. Most of Association Rule Mining (ARM) algorithms start by searching for frequent itemsets by scanning the whole database repeatedly and enumerating the occurrences of each candidate itemset. In data mining problems, the size of ...