SQL based frequent pattern mining without candidate generation

Shang, Xuequn; Sattler, Kai Uwe; Geist, Ingolf

doi:10.1145/967900.968027

Cited by 22 publications

(34 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For data streams ， we can construct a 2-tuple, H={U, N}, Like constructing an FP-tree in frequent pattern mining [12], we call the final data structure we used to summarize the data streams DG(Dense Grid)tree.…”

Section: The Synopsis Ofmentioning

confidence: 99%

“…This process is similar to FP-growth which is used in frequent pattern mining [12], we call it DG-growth. The whole process is as follows： Algorithm 2(DG-growth: Search dense units with DG-tree by dense unit growth)…”

Section: Searching For Dense Grid Unitsmentioning

confidence: 99%

“…In 2004, Aggarwal, Han, and Yu developed a new algorithm named HPStream [12]. This is an improved version of CluStream.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clustering high dimensional data streams at multiple time granularities

Yan

Shen

2008

2008 3rd IEEE Conference on Industrial Electronics and Applications

View full text Add to dashboard Cite

In this paper, we extend our DGStream (Dense Grid-tree based data stream clustering) method which is developed recently [14] and propose a new method DGMStream (Dense Grid-tree based multiple time granularity adaptable data stream clustering) to cluster dynamic data streams. In DGMStream, we incorporate the technique of tilted time window in DGStream to find clusters for data streams over multiple time granularities. Implementation results show that this method has a better cluster purity and scalability than other methods.

show abstract

Section: The Synopsis Ofmentioning

confidence: 99%

Section: Searching For Dense Grid Unitsmentioning

confidence: 99%

See 1 more Smart Citation

Clustering high dimensional data streams at multiple time granularities

Yan

Shen

2008

2008 3rd IEEE Conference on Industrial Electronics and Applications

View full text Add to dashboard Cite

show abstract

“…The FP-growth algorithm is the most popular method which is developed by Han J. et al in [4,5]. The FP-growth algorithm mines frequent itemsets without generating candidate sets and scans database only twice.…”

Section: Introductionmentioning

confidence: 99%

Frequent itemsets mining using vertical index list

Sahaphong

2009

2009 2nd IEEE International Conference on Computer Science and Information Technology

View full text Add to dashboard Cite

In this paper, I propose a new approach to mine all frequent itemsets that performs database scanning only once to construct data structure. This structure uses the conceptual of vertical data layout to contain transaction data. The changing of minimal support is not effected by the data structure and rescan of database is not required. The proposed algorithm has the ability to find frequent itemsets without generation of candidate itemsets. It obtains complete and correct frequent itemsets. The examples of all definitions and correctness proving are provided.

show abstract

“…The core algorithm of the pattern growth approach is the Frequent Pattern growth (FP-growth) proposed by Han et al, [3]. In this method, the database is converted to a compact representation in the form of a tree called Frequent Pattern tree (FP-tree), which is much smaller in size than the original database.…”

Section: Related Workmentioning

confidence: 99%

PLT- Positional Lexicographic Tree: A New Structure for Mining Frequent Itemsets

Boukerche

Samarah

2006 International Conference on Parallel Processing Workshops (ICPPW'06)

View full text Add to dashboard Cite

Association rules have proved their influence in different industrial fields, where their goal is to identify the relations existing among the events that are stored in large databases. However, in order to enumerate the association rules, there is a need to identify the frequent set of itemsets (i.e. those events that occur together in a sufficient number of transactions). In this paper, a new representation structure for the data stored in any transactional database is proposed. This structure, which we refer to as Positional Lexicographic Tree (PLT), provides an efficient mechanism for subset checking based on a summary of the data extracted from the database. This makes PLT a promising tool for most of the existing data mining approaches. Moreover, our proposed PLT structure regulates the data in the database so that they can be applicable to compression and indexing techniques, which makes PLT suitable for supporting large databases. First, we introduce the PLT construction process, then highlight the different mining approaches that can be modulated to take advantage of PLT. We then present our algorithm and finally prove its correctness.

show abstract

SQL based frequent pattern mining without candidate generation

Cited by 22 publications

References 15 publications

Clustering high dimensional data streams at multiple time granularities

Clustering high dimensional data streams at multiple time granularities

Frequent itemsets mining using vertical index list

PLT- Positional Lexicographic Tree: A New Structure for Mining Frequent Itemsets

Contact Info

Product

Resources

About