An Efficient Subset-Lattice Algorithm for Mining Closed Frequent Itemsets in Data Streams

Abstract

Online mining association rules over data streams is an important issue in the area of data mining, where an association rule means that the presence of some items in a transaction will imply the presence of other items in the same transaction. There are many applications of using association rules in data streams, such as market analysis, network security, sensor networks and web tracking. Mining closed frequent itemsets is a further work of mining association rules, which aims to find the subsets of frequent itemsets that could extract all frequent itemsets. Formally, a closed frequent itemset is an frequent itemset which has no superset with the same support as it. Since data streams are continuous, high-speed, and unbounded, archiving everything from data streams is impossible. That is, we can only scan once for the data streams and it is a main-memory database. Therefore, previous algorithms to mine closed frequent itemsets in the traditional database are not suitable for data streams. On the other hand, many applications are interested in the most recent data, and there is a model to deal with the most recent data in data streams, called Sliding Window Model, which acquires the recent data with a window size meets this characteristic. One of well-known algorithms for mining closed frequent itemsets which based on the sliding window model is the NewMoment algorithm. However, the NewMoment algorithm could not efficiently mine closed frequent itemsets in data streams, since they will generate closed frequent itemsets and many unclosed frequent itemsets. Moreover, when data in the sliding window is incrementally updated, the NewMoment algorithm needs to reconstruct the whole tree structure. Therefore, in this thesis, we propose a sliding window approach, the Subset-Lattice algorithm, which embeds the subset property into the lattice structure to efficiently mine closed frequent itemsets. Basically, Our proposed algorithm considers five kinds of set concepts : (1) equivalent, (2) superset, (3) subset, (4) intersection, (5) empty relation, when data items are inserted. We judge closed frequent itemsets without generating unclosed frequent itemsets by these five kinds of set concepts. Moreover, when data in the sliding window is incrementally updated, our Subset-Lattice algorithm will not reconstruct the whole lattice structure. Therefore, our Subset-Lattice algorithm is more efficient than the Moment algorithm. Furthermore, we use the bit-pattern to represent the itemsets, and use bit-operations to speed up the set-checking. From our simulation results, we show that our Subset-Lattice algorithm needs less memory and less processing time than the NewMoment algorithm. When window slides, the execution time could be saved up to 50%.

Mining Closed Item sets from Tuple-Evolving Data Streams

“…The various instances of auction bids are projected in Figure 1. From the Figure 1(a), it can be seen that second instance does not have the transactions of 1,2,3 and 4 which are expired when window moves from W(UT1-12) to W (5)(6)(7)(8)(9)(10)(11)(12)(13)(14). And other instances also visualized in the same.Traditional data stream FIM-techniques of closed itemsets are mentioned in Figure 1(a) for the consideration minimum support 40%.…”

“…It is under the performance of too many intersection operations. Chang et al [11] proposed Subsetlattice, combinations of bit vector Tid's to derive CIM. But it is limited to single window.…”

A new efficient SIF-based FCIL (SIF–FCIL) mining algorithm in predicting the crime locations

“…Since the calculation time was low, the performance of the clustering result did not meet the satisfactory level. Chang, Li, and Peng (2012) have recommended the subset-lattice algorithm that has entrenched the characteristics of subsets into the lattice structure in order to mine closed frequent itemsets over a data stream sliding window efficiently. The whole lattice structure is updated instead of the reconstruction process at the time of the addition of data in the sliding window.…”

“…An amazing inference is that mining frequent closed itemsets predominantly wields the identical prowess of mining the entire set of frequent itemsets, with the sterling strength of significantly shrinking the surplus rules to be produced and stepping up the efficiency and effectiveness of mining (Jian et al, 2000). A closed frequent itemset, in fact, is a frequent itemset having no superset with the identical support it has (Chang, Li, Peng, & Wang, 2013). Frequent closed itemset mining (FCIM) has appeared on the arena as the amazingly advanced approach in a the number of data mining applications for characterising the valuable extracting patterns (or itemsets) from the entire colossal candidate patterns within a transaction database for addressing the thorny issue of a frequent itemset mining (FIM).…”