Many researches has focused mainly on how to expedite the search for frequently co-occurring groups of items in “shopping cart” and less attention has been paid to the methods that exploit these “frequent itemsets” for prediction purposes. This study contributes to this task by proposing a technique that uses the partial information about the contents of a shopping cart for the prediction of wh...

Today there are several skilled algorithms to mine frequent patterns. Frequent item set mining provides the associations and correlations among items in large transactional or relational database. In this paper a new approach to mine frequent pattern in spatial database using TFP-tree is proposed. The proposed approach generates a TFP-tree that specifies the generations of frequent patterns. Ou...

Data mining is the process of mining new non trivial and potentially valuable information from large data basis. Data mining has been used in the analysis of customer transaction in retail research where it is termed as market basket analysis. Earlier data mining methods concentrated more on the correlation between the items that occurs more frequent in the transaction. In frequent itemset mini...

Data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data stored in repositories, corporate databases, and data warehouses. In Data mining field, the primary task is to mine frequent item sets from a transaction database using Association Rule Mining (ARM).Whereas the extraction of frequent patterns has focused the majo...

Based on the intuition that frequent patterns can be used to predict the next few items that users would want to access, sequential pattern mining-based next-items recommendation algorithms have performed well in empirical studies including online product recommendation. However, most current methods do not perform personalized sequential pattern mining, and this seriously limits their capabili...

There have been many recent studies on sequential pattern mining. The sequential pattern mining on progressive databases is relatively very new, in which we progressively discover the sequential patterns in period of interest. Period of interest is a sliding window continuously advancing as the time goes by. As the focus of sliding window changes , the new items are added to the dataset of inte...

Web Usage Mining is the application of data mining techniques to large web log databases in order to extract usage patterns. However, most of the previous studies on usage patterns discovery just focus on mining intra-transaction associations, i.e., the associations among items within the same user’s transactions. A cross-transaction association rule describes the association relationships amon...

Background: Wolf Motor Function Test (WMFT) is used in the assessment of upper extremity motor function in stroke patients. This scale contains 15 items and assesses joint-segment movements and functional tasks. Objectives: Translation and assessment of internal consistency and reliability of the Persian version of WMFT in Iranian stroke patients. Materials and Methods: After translation ...

A fundamental task of data mining is to mine frequent itemsets. Since the number of frequent itemsets may be large, a compact representation, namely the max frequent itemsets, has been introduced. On the other hand, the concept of generalized itemsets was proposed. Here, the items form a taxonomy. Although the transactional database only contains items in the leaf level of the taxonomy, a gener...