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Data intensive peer-to-peer (P2P) networks are becoming increasingly popular in applications like social networking, file sharing networks, etc. Data mining in such P2P environments is the new generation of advanced P2P applications. Unfortunately, most of the existing data mining algorithms do not fit well in such environments since they require data that can be accessed in its entirety. It also is not easy due to the requirements of online transactional data streams. In this paper, we have developed a local algorithm for tracing frequent item sets over a P2P network. The performance of the proposed algorithm is comparatively tested and analyzed through a series of experiments. [ABSTRACT FROM AUTHOR]

Abstract: Frequent itemset mining from data streams is an important data mining problem with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. However, it is also a difficult problem due to the unbounded, high-speed and continuous characteristics of streaming data. Therefore, extracting frequent itemsets from more recent data can enhance the analysis of stream data. In this paper, we propose an efficient algorithm, called Max-FISM (Maximal-Frequent Itemsets Mining), for mining recent maximal frequent itemsets from a high-speed stream of transactions within a sliding window. According to our algorithm, whenever a new transaction is inserted in the current window only its maximum itemset should be inserted into a prefix tree-based summary data structure called Max-Set for maintaining the number of independent appearance of each transaction in the current window. Finally, the set of recent maximal frequent itemsets is obtained from the current Max-Set. Experimental studies show that the proposed Max-FISM algorithm is highly efficient in terms of memory and time complexity for mining recent maximal frequent itemsets over high-speed data streams. [Copyright &y& Elsevier]

Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffective for a data mining system to analyze them. In the end, it produces a huge number of fuzzy associations. This is particularly true for datasets whose attributes are highly correlated. The huge number of fuzzy associations makes it very difficult for a human user to analyze them. Existing research has shown that most of the discovered rules are actually redundant or insignificant. In this paper, we propose a novel technique to overcome these problems; we are preprocessing the data tuples by focusing on similar behaviour attributes and ontology. Finally, the efficiency and advantages of this algorithm have been proved by experimental results. [ABSTRACT FROM AUTHOR]

One-versus-all (OVA) classifiers learn κ individual binary classifiers, each distinguishing the instances of a single class from the instances of all other classes. To classify a new instance, the κ classifiers are run, and the one that returns the highest confidence is chosen. Thus, OVA is different from existing data stream classification schemes whose majority use multiclass classifiers, each discriminating among all the classes. This paper advocates some outstanding advantages of OVA for data stream classification. First, there is low error correlation and, hence, high diversity among OVA's component classifiers, which leads to high classification accuracy. Second, OVA is adept at accommodating new class labels that often appear in data streams. However, there also remain many challenges to deploy traditional OVA for classifying data streams. First, traditional OVA does not handle concept change, a key feature of data streams. Second, as every instance is fed to all component classifiers, OVA is known as an inefficient model. Third, OVA's classification accuracy is adversely affected by the imbalanced class distributions in data streams. This paper addresses those key challenges and consequently proposes a new OVA scheme that is adapted for data stream classification. Theoretical analysis and empirical evidence reveal that the adapted OVA can offer faster training, faster updating, and higher classification accuracy than many existing popular data stream classification algorithms. We expect these results to be of interest to researchers and practitioners because they suggest a simple but very elegant and effective alternative to existing classification schemes for data streams. [ABSTRACT FROM AUTHOR]

The purpose of the intrusion detection system (IDS) database is to detect transactions that access data without permission. This paper proposes a novel approach to identifying malicious transactions. The approach concentrates on two aspects of database transactions: (1) dependencies among data items and (2) variations of each individual data item which can be considered as time-series data. The advantages are threefold. First, dependency rules among data items are extended to detect transactions that read or write data without permission. Second, a novel behaviour similarity criterion is introduced to reduce the false positive rate of the detection. Third, time-series anomaly analysis is conducted to pinpoint intrusion transactions that update data items with unexpected pattern. As a result, the proposed approach is able to track normal transactions and detect malicious ones more effectively than existing approaches. [ABSTRACT FROM AUTHOR]