To early detect and defend the threats in the Internet caused by botnet, darknet monitoring is very important to understand various botnet activities. However, common illegal accesses by ordinary malwares makes such detection difficult. In this paper, in order to remove such accesses by ordinary malwares from the results of network monitoring, we propose a data screening method based on finding...

This chapter will provide a detailed survey of frequent pattern mining algorithms. A wide variety of algorithms will be covered starting from Apriori. Many algorithms such as Eclat, TreeProjection, and FP-growth will be discussed. In addition a discussion of several maximal and closed frequent pattern mining algorithms will be provided. Thus, this chapter will provide one of most detailed surve...

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...

Integration of data mining and database management systems could significantly ease the process of knowledge discovery in large databases. We consider implementations of frequent itemset mining algorithms, in particular pattern-growth algorithms similar to the top-down FP-growth variations, tightly coupled to relational database management systems. Our implementations remain within the confines...

Pushing monotone constraints in frequent pattern mining can help pruning the search space, but at the same time it can also reduce the effectiveness of anti-monotone pruning. There is a clear tradeoff. Is it better to exploit more monotone pruning at the cost of less antimonotone pruning, or viceversa? The answer depends on characteristics of the dataset and the selectivity of constraints. In t...

Frequent pattern mining is a core data mining operation and has been extensively studied over the last decade. Recently, mining frequent patterns over data streams have attracted a lot of research interests. Compared with other streaming queries, frequent pattern mining poses great challenges due to high memory and computational costs, and accuracy requirement of the mining results. In this cha...

Das Aufdecken unbekannter Zusammenhänge zählt zu einer der wichtigsten Aufgaben im Data Mining. Für das Problem des Frequent Pattern Mining über statischen Daten finden sich daher in der Literatur eine Vielzahl an Lösungen. Die Integration von Sensorik in nahezu jeden Lebensbereich führt allerdings zu Datenmengen, welche mittels der klassischen Verfahren zumeist nicht mehr bewältigt werden könn...

The frequent-pattern-tree (FP-tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It was used to compress a database into a tree structure which stored only large items. It, however, needed to process all transactions in a batch way. In real-world applications, new transactions are usually inserted into databases. In this paper, we thus att...

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