Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability

Efficiently Using Prime-Encoding for Mining Frequent Itemsets in Sparse Data

In the data mining field, data representation turns out to be one of the major factors affecting mining algorithm scalability. Mining Frequent Itemsets (MFI) is a data mining problem that is heavily affected by this fact. The vertical approach is one of the successful data representations adopted for MFI problem. The main advantage of this approach is support for fast frequency counting via joining operations. Recently, an encoding method called prime-encoding is proposed as an enhancement for the vertical approach [10]. The performance study introduced in [10] confirmed the high quality of prime-encoding based vertical mining of frequent sequence over other vertical and horizontal ones in terms of space and time. Though sequence mining is more general than itemset mining, this paper presents a prime-encoding based vertical mining of frequent itemsets with new optimizations and a new re-encoding method that further enhance memory and speed. The experimental results show that prime encoding based vertical itemset mining is suitable for high-dimensional sparse data

Frequent itemset mining on multiprocessor systems

Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent years, there have been many frequent-itemset mining algorithms proposed, which however (1) often have high memory requirements and (2) do not exploit the large degrees of parallelism provided by modern multiprocessor systems. The high memory requirements arise mainly from inefficient data structures that have only been shown to be sufficient for small datasets. For large datasets, however, the use of these data structures force the algorithms to go out-of-core, i.e., they have to access secondary memory, which leads to serious performance degradations. Exploiting available parallelism is further required to mine large datasets because the serial performance of processors almost stopped increasing. Algorithms should therefore exploit the large number of available threads and also the other kinds of parallelism (e.g., vector instruction sets) besides thread-level parallelism. In this work, we tackle the high memory requirements of frequent itemset mining twofold: we (1) compress the datasets being mined because they must be kept in main memory during several mining invocations and (2) improve existing mining algorithms with memory-efficient data structures. For compressing the datasets, we employ efficient encodings that show a good compression performance on a wide variety of realistic datasets, i.e., the size of the datasets is reduced by up to 6.4x. The encodings can further be applied directly while loading the dataset from disk or network. Since encoding and decoding is repeatedly required for loading and mining the datasets, we reduce its costs by providing parallel encodings that achieve high throughputs for both tasks. For a memory-efficient representation of the mining algorithms’ intermediate data, we propose compact data structures and even employ explicit compression. Both methods together reduce the intermediate data’s size by up to 25x. The smaller memory requirements avoid or delay expensive out-of-core computation when large datasets are mined. For coping with the high parallelism provided by current multiprocessor systems, we identify the performance hot spots and scalability issues of existing frequent-itemset mining algorithms. The hot spots, which form basic building blocks of these algorithms, cover (1) counting the frequency of fixed-length strings, (2) building prefix trees, (3) compressing integer values, and (4) intersecting lists of sorted integer values or bitmaps. For all of them, we discuss how to exploit available parallelism and provide scalable solutions. Furthermore, almost all components of the mining algorithms must be parallelized to keep the sequential fraction of the algorithms as small as possible. We integrate the parallelized building blocks and components into three well-known mining algorithms and further analyze the impact of certain existing optimizations. Our algorithms are already single-threaded often up an order of magnitude faster than existing highly optimized algorithms and further scale almost linear on a large 32-core multiprocessor system. Although our optimizations are intended for frequent-itemset mining algorithms, they can be applied with only minor changes to algorithms that are used for mining of other types of itemsets

Optimizing Frequency Queries for Data Mining Applications

Data mining algorithms use various Trie and bitmap-based representations to optimize the support (i.e., frequency) counting performance. In this paper, we compare the memory requirements and support counting performance of FP Tree, and Compressed Patricia Trie against several novel variants of vertical bit vectors. First, borrowing ideas from the VLDB domain, we compress vertical bit vectors using WAH encoding. Second, we evaluate the Gray code rank-based transaction reordering scheme, and show that in practice, simple lexicographic ordering, obtained by applying LSB Radix sort, outperforms this scheme. Led by these results, we propose HDO, a novel Hamming-distance-based greedy transaction reordering scheme, and aHDO, a linear-time approximation to HDO. We present results of experiments performed on 15 common datasets with varying degrees of sparseness, and show that HDO- reordered, WAH encoded bit vectors can take as little as 5% of the uncompressed space, while aHDO achieves similar compression on sparse datasets. Finally, with results from over a billion database and data mining style frequency query executions, we show that bitmap-based approaches result in up to hundreds of times faster support counting, and HDO-WAH encoded bitmaps offer the best space-time tradeoff

A STUDY ON EFFICIENT DATA MINING APPROACH ON COMPRESSED TRANSACTION

Data mining can be viewed as a result of the natural evolution of information technology. The spread of computing has led to an explosion in the volume of data to be stored on hard disks and sent over the Internet. This growth has led to a need for data compression, that is, the ability to reduce the amount of storage or Internet bandwidth required to handle the data. This paper analysis the various data mining approaches which is used to compress the original database into a smaller one and perform the data mining process for compressed transaction such as M2TQT,PINCER-SEARCH algorithm, APRIOR

Set Representation for Rule Generation Algorithms

The task of mining the association rule has become one of the most widely used discovery pattern methods in Knowledge Discovery in Databases (KDD). One such task is to represent the itemset in the memory. The representation of the itemset largely depend on the type of data structure that is used for storing them. Computing the process of mining the association rule im- pacts the memory and time requirement of the itemset. With the increase in the dimensionality of data and datasets, mining such large volume of datasets will be difficult since all these itemsets cannot be placed in the main memory. As representation of an itemset greatly affects the efficiency of the rule mining association, a compact and compress representation of an itemset is needed. In this paper, a set representation is introduced which is more memory and cost efficient. Bitmap representation takes one byte for an element but the set representation uses one bit. The set representation is being incorporated in Apriori Algorithm. Set representation is also being tested for different rule generation algorithms. The complexities of these different rule generation algorithms using set representation are being compared in terms of memory and time execution

A New Data Layout For Set Intersection on GPUs

Set intersection is the core in a variety of problems, e.g. frequent itemset mining and sparse boolean matrix multiplication. It is well-known that large speed gains can, for some computational problems, be obtained by using a graphics processing unit (GPU) as a massively parallel computing device. However, GPUs require highly regular control flow and memory access patterns, and for this reason previous GPU methods for intersecting sets have used a simple bitmap representation. This representation requires excessive space on sparse data sets. In this paper we present a novel data layout, "BatMap", that is particularly well suited for parallel processing, and is compact even for sparse data. Frequent itemset mining is one of the most important applications of set intersection. As a case-study on the potential of BatMaps we focus on frequent pair mining, which is a core special case of frequent itemset mining. The main finding is that our method is able to achieve speedups over both Apriori and FP-growth when the number of distinct items is large, and the density of the problem instance is above 1%. Previous implementations of frequent itemset mining on GPU have not been able to show speedups over the best single-threaded implementations.Comment: A version of this paper appears in Proceedings of IPDPS 201