Abstract. In this paper, we study the problem of finding frequent items in a continuous stream of items. A new frequency measure is introduced, based on a flexible window length. For a given item, its current frequency in the stream is defined as the maximal frequency over all windows from any point in the past until the current state. We study the properties of the new measure, and propose an incremental algorithm that allows to produce the current frequency of an item immediately at any time. It is shown experimentally that the memory requirements of the algorithm are extremely small for many different realistic data distributions. 1

Abstract — Online mining of streaming data is one of the most important issues in data mining. In this paper, we proposed an efficient one-pass algorithm, called MFI-TransSW (Mining Frequent Itemsets over a Transaction-sensitive Sliding Window), to mine the set of all frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. The experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithms for mining frequent itemsets over recent data streams.

Abstract- Mining frequent patterns over data streams is an interesting problem due to its wide application area. In this study, a novel method for sliding window frequent patterns mining over data streams is proposed. This method utilizes a compressed and memory efficient tree data structure to store and to maintain sliding window transactions. The method dynamically reconstructs and compresses tree data structure to control the amount of memory usage. Moreover, the mining task is efficiently performed using the data structure when a user issues a mining request. The mining process reuses the tree structure to extract frequent patterns and does not need additional memory requirement. Experimental evaluations on real datasets show that our proposed method outperforms recently proposed sliding window based algorithms.

Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent closed itemsets, but they are mainly intended for traditional transaction databases and thus do not take data stream characteristics into consideration. In this paper, we propose a novel approach for mining closed frequent itemsets over data streams. It computes and maintains closed itemsets online and incrementally and can output the current closed frequent itemsets in real time based on users’ specified thresholds. Experimental results show that our proposed method is both time and space efficient, has good scalability as the number of transactions processed increases and adapts very rapidly to the change in data streams. 1.

In recent years there has been a growing interest in the study and analysis of flows of so-called data streams. Typical examples of such streams include Internet traffic data and continuous sensor readings. Traditional data mining approaches are not suitable for mining such streams, because they assume static data stored in a database, whereas streams are continuous, high speed, and unbounded. Therefore, streams must be analyzed as they are produced and high quality, online results need to be guaranteed. Until now, most pattern mining techniques focus either on non-streaming data, or only consider very simple patterns, such as identifying the hot items from one stream, or constantly maintaining the frequencies in a window sliding over the stream. The challenging task we set forward in this project is to extend the existing state-of-the-art techniques into two, orthogonal directions: on the one hand, the mining of more complex patterns in streams, such as sequential patterns and evolving graph patterns and on the other hand, more natural stream support measures taking into account the temporal nature of most data streams. The developed techniques will be tested on real-life data, such as social network data and the World-Wide Web. Next to those datasets, in the project we will have access to the data streams generated by a sensor network mounted on a large bridge in The Netherlands.

Abstract. Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered: data streams describe complex objects modeled by multiple database relations. A multi-relational data mining algorithm is proposed to efficiently discover approximate relational frequent patterns over a sliding time window of a complex data stream. The effectiveness of the method is proved on application to the Internet packet stream. 1