Automatic tagging techniques are important for many applications such as searching and recommendation, which has attracted many researchers' attention in recent years. Existing methods mainly rely on users' tagging behavior or items' content information for tagging, yet users' consuming behavior is ignored. In this paper, we propose to leverage such information and introduce a probabilistic model called joint-tagging LDA to improve tagging accuracy. An effective algorithm based on Zero-Order Collapsed Variational Bayes is developed. Experiments conducted on a real dataset demonstrate that joint-tagging LDA outperforms existing competing methods.

Finding frequent items or top-k items in data streams is a basic mining task with a wide range of applications. There are lots of algorithms proposed to enhance the performance of these algorithms, whereas not much effort has been made to make use of hierarchical information held by items in data stream. In this paper, we try to improve the accuracy of finding frequent items using hierarchical information in taxonomy. To do that, we propose a method called Merge. According to the strategy, we design and implement an algorithm, named FISH_Merge. In order to evaluate the performance of the algorithm, we propose three new measures for testing, and develop a hierarchical stream data generator. After conducting a comprehensive experimental study, we conclude that accuracy of FISH_Merge is better than algorithms without using hierarchical information under same amount of memory. In the meantime, our algorithm can also provide some information of higher level items.

Many real world applications deal with transactional data, characterized by a huge number of transactions (tuples) with a small number of dimensions (attributes). However, there are some other applications that involve rather high dimensional data with a small number of tuples. Examples of such applications include bioinformatics, survey-based statistical analysis, text processing, and so on. High dimensional data pose great challenges to most existing data mining algorithms. Although there are numerous algorithms dealing with transactional data sets, there are few algorithms oriented to very high dimensional data sets with a relatively small number of tuples.

It is well recognized that data cubing often produces huge outputs. Two popular efforts devoted to this problem are (1) iceberg cube, where only significant cells are kept, and (2) closed cube, where a group of cells which preserve roll-up/drill-down semantics are losslessly compressed to one cell. Due to its usability and importance, efficient computation of closed cubes still warrants a thorough study. In this paper, we propose a new measure, called closedness, for efficient closed data cubing. We show that closedness is an algebraic measure and can be computed efficiently and incrementally. Based on closedness measure, we develop an an aggregation-based approach, called C-Cubing (i.e., Closed-Cubing), and integrate it into two successful iceberg cubing algorithms: MM-Cubing and Star-Cubing. Our performance study shows that C-Cubing runs almost one order of magnitude faster than the previous approaches. We further study how the performance of the alternative algorithms of C-Cubing varies w.r.t the properties of the data sets.

Data sets of very high dimensionality, such as microarray data, pose great challenges on efficient processing to most existing data mining algorithms. Recently, there comes a row-enumeration method that performs a bottom-up search of row combination space to find corresponding frequent patterns. Due to a limited number of rows in microarray data, this method is more efficient than column enumerationbased algorithms. However, the bottom-up search strategy cannot take an advantage of user-specified minimum support threshold to effectively prune search space, and therefore leads to long runtime and much memory overhead. In this paper we propose a new search strategy, top-down mining, integrated with a novel rowenumeration tree, which makes full use of the pruning power of the minimum support threshold to cut down search space dramatically. Using this kind of searching strategy, we design an algorithm, TD-Close, to find a complete set of frequent closed patterns from very high dimensional data. Furthermore, an effective closeness-checking method is also developed that avoids scanning the dataset multiple times. Our performance study shows that the TD-Close algorithm outperforms substantially both Carpenter, a bottom-up searching algorithm, and FPclose, a column enumeration-based frequent closed pattern mining algorithm.