CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach

Erwin, Alva; Gopalan, Raj P.; Achuthan, N. R.

doi:10.1109/cit.2007.120

Cited by 78 publications

(78 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this stare C. CTUMy own [31] counselled an criteria that is competent above Two-Phase criteria merely in heavy data basis after the minimum request check is extremely low In the subsequent data basis check out, it discovers all the 2-factor deal heavy custom item set and reliant on that produces the applicants for 3-element deal heavy custom item sets and so on. But yet their methods are altered alongside the level-wise applicant generation-and-examine subject and needs several data basis examinations reliant on the highest probable length of the applicant styles.…”

Section: Item Set Mining Using Functional Parameters or Utilitiesmentioning

confidence: 99%

“…They cannot maintain the downwards closing residence of Apriori. The great application exploration designs were described in [28], [29], [30], [31] and [32]. They used a heuristic to figure out whether an item set should be considered as a candidate Item set.…”

Section: Item Set Mining Using Functional Parameters or Utilitiesmentioning

confidence: 99%

“…Hence, an enhanced criteria known as the Categorized Pressure tree (SC tree) where organization rules can be excavated in a bottom-up style instead of bi-directional or recursive is suggested. Tree Based [22], [25], [26], [31], [33], [34], [35], [51], [52], [53], [54], [55], [56], [57] Apriori Based [23], [28], [29], [30], [32], [37], [38], [39], [40], [41], [42], [43], [47], [48], [49], [50] Candidature [23], [28], [29], [30], [32], [37], [38], [39], [40], [41], [42], [43], [47], [48], [49], [50] support constraint [22], [25], …”

Section: Mining Frequent Itemsets With No Candidaturementioning

confidence: 99%

See 2 more Smart Citations

Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art

Muralidhar¹,

Pattabiraman²

2013

IJCA

View full text Add to dashboard Cite

Identifying the association rules in colossal datasets is possessing elevated level of presence in data mining or data exploration. As a consequence, countless algorithms are approximated to deal alongside this issue. The two setbacks ambitious considering this outlook are: ascertaining all frequent item sets and to produce limits from them. This document is for the most portions aimed at pondering of past scrutiny, present useful rank and to ascertain the gaps of them alongside present ambitious information. Here, early subject, as it acquires extra time to process, is computationally expensive. Current discover targeted on these algorithms and their connected issues.Keywords: Frequent Itemset Mining, utility mining, Frequent pattern mining, Association rules, Data mining INTRODUCTIONThere are a number of suggested methods for producing frequent item sets which differ in the way of spanning item set lattice, use of anti-monotone property and the way to deal with dataset. It provided in data indexing, category and clustering. Identifying frequent patterns is important in exploration organizations and connections. A substructure can be a sub-graph or sub-tree. If such substructure happens more than particular limit, it is called a regular architectural design of times. Let us talk about present position of this step such as the examined difficulties. Frequent patterns are item sets or substructures which happen in a dataset more than specified lowest no. Depending on these modifications, associate set of algorithms is explained the research differs from effective and scalable methods to most research frontiers; such as sequential, structured, correlative exploration, associative classification and frequent pattern clustering. For market basket research which examines client features from the organizations between things in the basket. In the function of association rule mining, frequent pattern mining is an important level that has been targeted at and in which amazing upgrades have been made [1]. TAXONOMYThe preliminary criteria recommended were AIS, by Agrawal et al [1]. As there are large number, Apriori seemed to be better criteria in next creation, which completely contains the infrequent itemsets. Moreover, information components for servicing are not specified. SETM, which uses SQL, is another criterion which symbolizes frequent item set by means of Apriori [1] criteria enhanced the research over frequent pattern exploration. Of information source goes also decremented of single products in information source which may type big no. Another disadvantage is that it follows tupleby-tuple strategy after every deal which is an expense. Of item sets, it is difficult to create scalable criteria for it. It uses hash tree to save reverse. [28]. Here, we recognized the design recognition techniques depending on their resemblances. In Kth successfully pass, matters of K item sets are acquired. But, it goes over the information source duration of lengthiest frequent item set times (n) [1] for organization conc...

show abstract

Section: Item Set Mining Using Functional Parameters or Utilitiesmentioning

confidence: 99%

Section: Item Set Mining Using Functional Parameters or Utilitiesmentioning

confidence: 99%

Section: Mining Frequent Itemsets With No Candidaturementioning

confidence: 99%

See 1 more Smart Citation

Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art

Muralidhar¹,

Pattabiraman²

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…The Apriori and such a compressed database into a set of conditional databases. There are two scans needed for mining all frequent itemsets [7]. It uses an extended prefix tree structure to store the database in a compressed form.…”

Section: Footprints Of Pattern Mining Algorithms For Association Rulesmentioning

confidence: 99%

A Novelty Approach for Finding Frequent Itemsets in Horizontal and Vertical Layout- HVCFPMINETREE

Meenakshi¹,

Alagarsamy²

2010

IJCA

View full text Add to dashboard Cite

“…The approach is especially suitable for the sparse database with short patterns. Erwin, Gopalan, and Achuthan [8] proposed the CTU-Mine algorithm based on the pattern growth approach [11]. The algorithm is more efficient than the Two-Phase method in the dense database with long patterns.…”

Section: Introductionmentioning

confidence: 99%

Mining High Utility Itemsets in Data Streams Based on the Weighted Sliding Window Model

Tsai¹

2014

IJDKP

View full text Add to dashboard Cite

show abstract

CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach

Cited by 78 publications

References 5 publications

Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art

Bench Marking Frequent Item set Mining Models and Algorithms: Current State of the Art

A Novelty Approach for Finding Frequent Itemsets in Horizontal and Vertical Layout- HVCFPMINETREE

Mining High Utility Itemsets in Data Streams Based on the Weighted Sliding Window Model

Contact Info

Product

Resources

About