Weighted frequent itemset mining is more practical than traditional frequent itemset mining, because it can consider different semantic significance (weight) of items. Many models and algorithms for mining weighted frequent itemsets have been proposed. These models assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of the items may vary wit...

Frequent itemset counting is the first step for most association rule algorithms and some classification algorithms. It is the process of counting the number of occurrences of a set of items that happen across many transactions. The goal is to find those items which occur together most often. Expressing this functionality in RDBMS engines is difficult for two reasons. First, it leads to extreme...

Several emerging applications warrant mining and discovering hidden frequent patterns in time series databases, e.g., sensor networks, environment monitoring, and inventory stock monitoring. Time series databases are characterized by two features: (1) The continuous arrival of data and (2) the time dimension. These features raise new challenges for data mining such as the need for online proces...

We examined sensitivity to grammatical sequences of colors in an artificial grammar learning task in a sample of 120 children aged between 5 and 8 years. The aim of the experiment was to test whether the children would preferentially learn the specific salient features of the items they were exposed to or the rules that generated these items. The children were divided into two experimental grou...

The vocabularies of endangered languages surrounded by more prestigious languages are gradually shrinking in size due to the influx of borrowed items. It is easy to observe that in such languages, starting from some frequency rank, the lower the frequency of a vocabulary item, the higher the probability of that item being a borrowed one. On the basis of the data from the Beserman dialect of Udm...

This lecture is about different techniques used for video streaming to improve performance and Quality of Experience (QoE). Specifically, we learned about ”neural adaptive video streaming with Pensieve” [1]. Pensieve adaptively selects bitrates (ABR) for future video chunks based on observations collected by client video players. It automatically learns ABR algorithms that adapt to a wide range...

-Association rule has been an area of active research in the field of knowledge discovery. Data mining researchers had improved upon the quality of association rule mining for business development by incorporating influential factors like value (utility), quantity of items sold (weight) and more for the mining of association patterns. In this paper, we propose an efficient approach to find maxi...

Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like u...

Association rule mining is one of the most important technique in data mining. Which wide range of applications It aims it searching for intersecting relationships among items in large data sets and discovers association rules. The important of association rule mining is increasing with the demand of finding frequent patterns from large data sources. The exploitation of frequent item set has be...