Learning concepts in parallel based upon the strategy of versionspace

Applies the technique of parallel processing to concept learning. A parallel version-space learning algorithm based upon the principle of divide-and-conquer is proposed. Its time complexity is analyzed to be O(k log₂n) with n processors, where n is the number of given training instances and k is a coefficient depending on the application domains. For a bounded number of processors in real situations, a modified parallel learning algorithm is then proposed. Experimental results are then performed on a real learning problem, showing that our parallel learning algorithm works, and being quite consistent with the results of theoretical analysis. We conclude that when the number of training instances is large, it is worth learning in parallel because of its faster execution     

An effective approach for maintenance of pre-large-based frequent-itemset lattice in incremental mining

Incremental mining has attracted the attention of many researchers due to its usefulness in online applications. Many algorithms have thus been proposed for incrementally mining frequent itemsets. Maintaining a frequent-itemset lattice (FIL) is difficult for databases with large numbers of frequent itemsets, especially huge databases, due to the storage of links of nodes in the lattice. However, generating association rules from a FIL has been shown to be more effective than traditional methods such as directly generating rules from frequent itemsets or frequent closed itemsets. Therefore, when the number of frequent itemsets is not huge (i.e., they can be stored in the lattice without excessive memory overhead), the lattice-based approach outperforms approaches which mine association rules from frequent itemsets/frequent closed itemsets. However, incremental algorithms for building FILs have not yet been proposed. This paper proposes an effective approach for the maintenance of a FIL based on the pre-large concept in incremental mining. The building process of a FIL is first improved using two proposed theorems regarding the paternity relation between two nodes in the lattice. An effective approach for maintaining a FIL with dynamically inserted data is then proposed based on the pre-large and the diffset concepts. The experimental results show that the proposed approach outperforms the batch approach for building a FIL in terms of execution time.     

An efficient bit-based feature selection method

Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.     

Maintenance of fast updated frequent pattern trees for record deletion

The Frequent-Pattern-tree (FP tree) is an efficient data structure for association-rule mining without generation of candidate itemsets. It was used to represent a database into a tree structure which stored only frequent items. It, however, needed to process all transactions in a batch way. In the past, Hong et al. thus proposed an efficient incremental mining algorithm for handling newly inserted transactions. In addition to record insertion, record deletion from databases is also commonly seen in real-applications. In this paper, we thus attempt to modify the FP-tree construction algorithm for efficiently handling deletion of records. A fast updated FP-tree (FUFP-tree) structure is used, which makes the tree update process become easier. An FUFP-tree maintenance algorithm for the deletion of records is also proposed for reducing the execution time in reconstructing the tree when records are deleted. Experimental results also show that the proposed FUFP-tree maintenance algorithm for deletion of records runs faster than the batch FP-tree construction algorithm for handling deleted records and generates nearly the same tree structure as the FP-tree algorithm. The proposed approach can thus achieve a good trade-off between execution time and tree complexity.     

An improved approach to find membership functions and multiple minimum supports in fuzzy data mining

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is called divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), and is designed for finding minimum supports, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item’s minimum support and membership functions. The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.     

Efficient maintenance of multiple-level association rules for deletion of records

Developing an efficient algorithm that can maintain discovered information as a database changes is quite important in data mining. Many proposed algorithms focused on a single level, and did not utilize previously mined information in incrementally growing databases. In the past, we proposed an incremental mining algorithm for maintenance of multiple-level association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this paper, we thus attempt to extend our previous approach to solve this issue. The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs. A pre-large itemset is not truly large, but promises to be large in the future. A lower support threshold and an upper support threshold are used to realize this concept. The two user-specified upper and lower support thresholds make the pre-large itemsets act as a gap to avoid small itemsets becoming large in the updated database when transactions are deleted. A new algorithm is thus proposed based on the concept to maintain discovered multiple-level association rules for deletion of records. The proposed algorithm doesn＇t need to rescan the original database until a number of records have been deleted. It can thus save much maintenance time.     

Linguistic frequent pattern mining using a compressed structure

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

     

Projection-based partial periodic pattern mining for event sequences

Partial periodic pattern mining is one of the important issues in the field of data mining due to its practical applications. A partial periodic pattern consists of some periodic and non-periodic events in a specific period length, and is repeated with high frequency in an event sequence. In the past, a max-subpattern hit set algorithm was developed to discover partial periodic patterns, but its drawback is spending a large amount of time in calculating frequency counts from the redundant candidate nodes. In this study, we thus adopt an efficient encoding strategy to speed up the efficiency of processing period segments in an event sequence, and combined with the projection method to quickly find the partial periodic patterns in the recursive process. Finally, the experimental results show the superior performance of the proposed approach.