Mining for Bitcoins is a high-risk high-reward activity. Miners, seeking to reduce their variance and earn steadier rewards, collaborate in so-called pooling strategies where they jointly mine for Bitcoins. Whenever some pool participant is successful, the earned rewards are appropriately split among all pool participants. Currently a dozen of different pooling strategies (i.e., methods for dis...

As is generally accepted, the most important feature that a Knowledge Discovery in Database (KDD) system must possess is, to be able to discover patterns that are “novel” and “potentially useful”. In order to allow KDD systems to make novelty and potential usefulness judgment, we extend our former work on discovering “potentially useful” patterns by proposing a formal definition of “novelty” ba...

High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent mining. Several high-utility approaches have already been proposed; however, they high computational costs and low efficiency. To solve this problem, a algorithm based on particle filter proposed. This approach first initializes population, consists sets. Th...

Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. From the past few years many methods have been proposed for generating high utility patterns, by this there are some problems as producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. The compact tree structure which...

Mining great service entities in trajectory database indicates to the exposure of entities with huge service like acquisition. The extensive number of contender entities degrades the mining achievement in terms of execution time and space stipulation. The position may become worse when the database consists of endless lengthy transactions or lengthy huge utility entity sets. In this paper, we u...

The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific ...

This paper presents an original text mining approach based on text segmented units, mixing data mining techniques and text linguistics. First, we describe the text units and their descriptors designed for detecting mistakes in scientific papers in English. These include text organisation, emphasizing the relative positions and the context of stylistic markers. Secondly, the paper explains the t...

Mining High Utility Itemsets (HUIs) is an important task with many applications. However, the set of HUIs can be very large, which makes HUI mining algorithms suffer from long execution times and huge memory consumption. To address this issue, concise representations of HUIs have been proposed. However, no concise representation of HUIs has been proposed based on the concept of generator despit...

Data mining has primarily focused on statistical properties of data alone and not necessarily on what could be done with the patterns. While there has been some work on measuring usefulness of patterns in decision making but not on using such measures for driving the mining process. We introduce a framework to mine clusters that support decision making. We use an extrinsic measure that evaluate...

This paper presents an overview of the data set that was used for the Cultural Microblog Contextualization Workshop at CLEF 2016 and more specifically for the task 1: tweet contextualization. In this paper we first present a descriptive analysis of the data: we consider the variables or features associated with the tweets and analyse them. Then we also analyse the tweet textual content. The res...