Intelligent Data Analysis: An International Journal

The set of frequent closed itemsets determines exactly the complete set of all frequent itemsets and is usually much smaller than the latter. This paper proposes an improved algorithm for mining frequent closed itemsets. Firstly, the index array is proposed, which is used for discovering those items that always appear together. Then, by using bitmap, an algorithm for computing index array is presented. Thirdly, based on the heuristic information provided by index array, frequent items, which co-occur together and share the same support, are merged together. Thus, initial generators are calculated. Finally, based on index array, reduced pre-set and reduced post-set are proposed. It is proved that the reduced pre-set and reduced post-set not only retain the function of pre-set and post-set, but also have smaller sizes. Therefore, the redundant items in pre-set and post-set are deleted, thus making it possible to save a lot of work related to inclusion check. The experimental results show that the proposed algorithm is efficient especially on dense dataset.

Anomaly detection is an important branch of the classification problem which has attracted much attention during the previous years. This, as well as the growing need for distributed data mining techniques, and concerns for privacy and security issues of gathering all distributed data in a central location, emphasizes the importance of the distributed anomaly detection problem, which has thus far received little attention. In this paper, we address the problem of detecting anomalies in horizontally distributed data, where only a limited ratio of the instances at each remote site are allowed to be shared, and no single entity is allowed to observe the whole dataset, neither at once nor incrementally. In our proposed method, local predictors are trained and association rules are extracted, using the difference between predicted and actual values on a context dataset. These association rules are used to represent normal and anomalous behaviors, while a final set of learners use these representations to detect anomalies. The contributions of our work are: 1) distributed anomaly detection, where (a) both data and process are distributed, (b) only a limited form of sharing is allowed and (c) no single entity is allowed to observe the whole data, in anyway, 2) solving the problem in cases where concept drifts might occur, 3) providing a solution which is able to handle potential dishonesty from participating entities, and 4) using association rules for anomaly detection, while maintaining the speed requirement in anomaly detection which is necessary in various applications. We have conducted a set of experiments, comparing our proposed method to other typical anomaly detection methods (oversampling, undersampling, SMOTE), which indicate the superiority of the proposed method, while preserving the privacy of participating datasets by avoiding the communication of all local samples to other local datasets.

Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, a new model based on cultural algorithm and fuzzy clustering procedure is proposed. This model promotes the formation and maintenance of subpopulations and implements the concept of cultural exchanges among them. The validity of this model is confirmed using some well-known test function. Moreover, an electromagnetic benchmark is solved to show the usefulness of the proposed approach in real world optimization.

The idea to automate the discovery of numeric laws goes back to early 1980's when some authors showed how to use to this end AI search techniques. Later, the community somewhat lost interest in this task, reasoning that it was unlikely that a computer program would ever "outperform" human intuition supported by background knowledge. Only recently did some authors manage to overcome this scepticism: their programs were able to discover new numeric laws in soft-science domains such as ecology and psychology. In the case study reported here, we describe a successful attempt to assist ocean chemists in their attempts to predict absorption rate of CO₂ in certain regions of the ocean. The system we have developed has found equations that outperform those suggested by field experts assisted by regression techniques. Our experience indicates that more attention should be paid to the following: the impact of spatio-temporal aspects, the existence of "hidden causes" (not directly reflected in a given set of variables), and the need for a field expert to post-process the results.

Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA.

The proposed method is based on the analysis of single-channel EEG using frequency and time domain methods. A clinical study was conducted on 22 patients to construct subsets of reference data corresponding to four well-defined anesthetic states: awake, moderate anesthesia, surgical anesthesia and isoelectric.

Statistical analysis of features was used to design input membership functions (MFs). The input space was partitioned with respect to the derived MFs and the training data was used to label the partitions and extract efficient fuzzy if-then rules. Consequently, the fuzzy rule-base index (FRI) is derived between 0 (isoelectric) to 100 (fully awake) using fuzzy inference engine and designed output MFs.

We also applied the same features to an adaptive network-based fuzzy inference system (ANFIS) derived without any prior knowledge. The results show that FRI correlates more with the clinically accepted DOA index, CSI™ (CSM, Danmeter, Denmark). In addition to this achievement the main idea behind this study is to simplify the mutual knowledge exchange between the human expert and the machine, leading to enhance both interpretability of the results and performance of the system.

We examined an identifier matching problem, where an identifier corresponds to pieces of information extracted from DNA samples associated with crime suspects or victims of accidents. Such a sample is matched to cases of a database in order to identify a person. The data we used had been encoded as integers of relevantly small intervals. We solved the identifier matching task by sorting the data and then making comparisons for a maximally reduced subset of data produced by sorting. We first constructed a simple baseline method with exhaustive search, its optimised version and compared their results to those of the sort-based method, which was superior to the former two.

In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same piece are obtained from changes in beat-level tempo and beat-level loudness, which over the time of the piece form a performance worm. From such worms, general performance alphabets can be derived, and pianists' performances can then be represented as strings. We show that when using the string kernel on this data, both kernel partial least squares and Support Vector Machines outperform the current best results. Furthermore we suggest a new method of obtaining feature directions from the Kernel Partial Least Squares algorithm and show that this can deliver better performance than methods previously used in the literature when used in conjunction with a Support Vector Machine.