Given a large collection of transactions containing items, a basic common data mining problem is to extract the so-called frequent itemsets (i.e., set of items appearing in at least a given number of transactions). In this paper, we propose a structure called free-sets, from which we can approximate any itemset support (i.e., the number of transactions containing the itemset) and we formalize t...

Data Mining and knowledge discovery is one of the important areas. In this paper we are presenting a survey on various methods for frequent pattern mining. From the past decade, frequent pattern mining plays a very important role but it does not consider the weight factor or value of the items. The very first and basic technique to find the correlation of data is Association Rule Mining. In ARM...

Traditional methods use a single minimum support threshold to find out the complete set of frequent patterns. However, in real word applications, using single minimum item support threshold is not adequate since it does not reflect the nature of each item. If single minimum support threshold is set too low, a huge amount of patterns are generated including uninteresting patterns. On the other h...

In this paper, we present a polynomial-time algorithm for TPQ (tree pattern queries) minimization without XML constraints involved. The main idea of the algorithm is a dynamic programming strategy to find all the matching subtrees within a TPQ. A matching subtree implies a redundancy and should be removed in such a way that the semantics of the original TPQ is not damaged. Our algorithm consist...

OBJECTIVE This study aims to display the quality of the clinical notes at Abha Psychiatric Hospital out-patient clinics. METHODS In this chart review study, a total of 380 charts were randomly collected, and reviewed for the clinical items that should be included in the psychiatric clinical notes. Each chart note quality was indicated as good, fair or poor based on means and standard deviatio...

The problem of identifying the most frequent items across multiple datasets has received considerable attention over the last few years. When storage is a scarce resource, the topic is already a challenge; yet, its complexity may be further exacerbated not only by the many independent data sources, but also by the dynamism of the data, i.e., the fact that new items may appear and old ones disap...

Search engines are being widely used by the web users. The search engine companies are concerned to produce best search results. Search logs are the records which records the interactions between the user and the search engine. Various search patterns, user’s behaviors can be analyzed from these logs, which will help to enhance the search results. Publishing these search logs to third party for...

Traditional pattern discovery approaches permit to identify frequent patterns expressed in form of conjunctions of items and represent their frequent co-occurrences. Although such approaches have been proved to be effective in descriptive knowledge discovery tasks, they can miss interesting combinations of items which do not necessarily occur together. To avoid this limitation, we propose a met...

The set of frequent closed itemsets determines exactly the complete set of all frequent itemsets and is usually much smaller than the latter. This paper proposes an improved algorithm for mining frequent closed itemsets. Firstly, the index array is proposed, which is used for discovering those items that always appear together. Then, by using bitmap, an algorithm for computing index array is pr...

We present deterministic sub-linear space algorithms for problems over update data streams, including, estimating frequencies of items and ranges, finding approximate frequent items and approximate φ-quantiles, estimating inner-products, constructing near-optimal B-bucket histograms and estimating entropy. We also present improved lower bound results for several problems over update data streams.