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Abstract-This paper addresses the problem of finding frequent closed patterns (FCP5) from very dense data sets. We introduce two compressed hierarchical FCP mining algorithms: C-Miner and B-Miner. The two algorithms compress the original mining space, hierarchically partition the whole mining task into independent subtasks, and mine each subtask progressively. The two algorithms adopt different task partitioning strategies: C-Miner partitions the mining task based on Compact Matrix Division, whereas B-Miner partitions the task based on Base Rows Projection. The compressed hierarchical mining algorithms enhance the mining efficiency and facilitate a progressive refinement of results. Moreover, because the subtasks can be mined independently, C-Miner and B-Miner can be readily paralleled without incurring significant communication overhead. We have implemented C-Miner and B-Miner, and our performance study on synthetic data sets and real dense microarray data sets shows their effectiveness over existing schemes. We also report experimental results on parallel versions of these two methods. [ABSTRACT FROM AUTHOR]

Multidimensional data points can be mapped to one-dimensional space to exploit single dimensional indexing structures such as the B+ -tree. In this article we present a Generalized structure for data Mapping and query Processing (GiMP), which supports extensible mapping methods and query processing. GiMP can be easily customized to behave like many competent indexing mechanisms for multidimensional indexing, such as the UB-Tree, the Pyramid technique, the iMinMax, and the iDistance. Besides being an extendible indexing structure, GiMP also serves as a framework to study the characteristics of the mapping and hence the efficiency of the indexing scheme. Specifically, we introduce a metric called mapping redundancy to characterize the efficiency of a mapping method in terms of disk page accesses and analyze its behavior for point, range and kNN queries. We also address the fundamental problem of whether an efficient mapping exists and how to define such a mapping for a given data set. [ABSTRACT FROM AUTHOR]

In this article, we present an efficient B+ -tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a B+-tree structure and KNN search to be performed using one-dimensional range search. The choice of partition and reference points adapts the index structure to the data distribution. We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization. [ABSTRACT FROM AUTHOR]

In this paper, we adapt and optimize the BD-tree for main memory data processing. We compare the memory-based BD-tree against the Bk-tree arid CSB+-tree. We present cost models for exact match query for these indexes, including L2 cache and translation lookahead buffer (TLB) miss model and execution time model. We also implemented these structures and conducted experimental study. Our analytical and experimental results show that a well-tuned BD-tree is superior in most cases. [ABSTRACT FROM AUTHOR]