We present a message-passing based parallel version of the Space Saving algorithm designed to solve the k–majority problem. The algorithm determines in parallel frequent items, i.e., those whose frequency is greater than a given threshold, and is therefore useful for iceberg queries and many other different contexts. We apply our algorithm to the detection of frequent items in both real and syn...

Mining frequent patterns has been studied popularly in data mining research. All of previous studies assume that items in a pattern are unordered. However, the order existing between items must be considered in some applications. In this paper, we first give the formal model of ordered patterns and discuss the problem of mining frequent ordered patterns. Base on our analyses, we present two eff...

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 (based...

Association rules is a recent data mining technique to discover affinities, in large transaction databases, between items frequently purchased together. It has been claimed that the discovery of frequent sets of items is well suited for applications of market basket analysis to discover regularities in the purchase behaviour of customers. In this study, we integrate the discovery of frequent it...

In this paper we compare several mechanisms for using distributional statistics to derive word class information. We contrast three different ways of computing statistics for independent left and right neighbours with the notion of a frequent frame. We also investigate the role of utterance boundaries as context items and weighting of frequency information in terms of the successful simulation ...

Due to various reasons transaction data often lack information about some items. This leads to the problem that some potentially interesting frequent item sets cannot be discovered, since by exact matching the number of supporting transactions may be smaller than the user-specified minimum. In this study we try to find such frequent item sets nevertheless by inserting missing items into transac...

Efficient frequent pattern mining algorithms are decisive for mining association rule. In this paper, we examine the matter of association rule mining for items in a massive database of sales transactions. Finding large patterns from database transactions has been suggested in many algorithms like Apriori, DHP, ECLAT, FP Growth etc. But here we have introduced newer algorithm called Improved Fr...

Weighted Frequent Pattern Mining (WFPM) has brought the notion of the weight of the items into the Frequent Pattern mining algorithms. WFPM is practically much efficient than the frequent pattern mining. Several Weighted Frequent Pattern Mining methods have been used. However, they do not deal with the interactive and incremental database. A IWFPTWU algorithm has been proposed to allow the user...

A straightforward approach to frequent pairs mining in transactional streams is to generate all pairs occurring in transactions and apply a frequent items mining algorithm to the resulting stream. The well-known counter based algorithms Frequent and Space-Saving are known to achieve a very good approximation when the frequencies of the items in the stream adhere to a skewed distribution. Motiva...

Frequent pattern mining is always an interesting research area in data mining to mine several hidden and previously unknown pattern. The better algorithms are always introduced and become the topic of interest. Association rule mining is an implication of the form X implies Y, where X is a set of antecedent items and Y is the consequent items. There are several techniques have been introduced i...