Efficient colossal pattern mining in high dimensional datasets

Sohrabi, Mohammad Karim; Barforoush, Ahmad Abdollahzadeh

doi:10.1016/j.knosys.2012.03.003

Cited by 47 publications

(12 citation statements)

References 25 publications

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“…Frequent pattern mining is one of the most common mining techniques used for materialized view selection. Several centralized, parallel, and distributed algorithms have been presented that attempt to improve pattern-mining efficiency [20][21][22][23]. Recently, some materialized view selection methods have tried to use the benefits of pattern mining to speed up the view-selection process [1,24,25].…”

Section: Related Workmentioning

confidence: 99%

TSGV: a table-like structure-based greedy method for materialized view selection in data warehouses

Sohrabi¹,

Azgomi²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Since a data warehouse deals with huge amounts of data and complex analytical queries, online processing and answering to users' queries in data warehouses can be a serious challenge. Materialized views are used to speed up query processing rather than direct access to the database in on-line analytical processing. Since the large number and high volume of views prevents all of the views from being stored, selection of a proper subset of views to materialization is inevitable. Proposing an appropriate method for selecting the optimal subset of views for materialization plays an essential role in increasing the efficiency of responding to data warehouse queries. In this paper, a greedy materialized view selection algorithm is represented, which selects a proper set of views for materialization from a novel table-like structure. The information in this table-like structure is extracted from a multivalue processing plan. This table-like structure-based greedy view selection (TSGV) method is evaluated using the queries of an analytical database, and the query-processing and view maintenance costs of the selected subset are both considered in this evaluation. The experimental results show that TSGV operates better than previously represented methods in terms of time.

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Section: Related Workmentioning

confidence: 99%

TSGV: a table-like structure-based greedy method for materialized view selection in data warehouses

Sohrabi¹,

Azgomi²

2017

Turk J Elec Eng & Comp Sci

Self Cite

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“…Due to the increasing number of such attacks, many researchers have tackled phishing. Due to the variety of data mining techniques [7], the increasing improvement of its methods [8][9][10][11][12][13][14][15], and the ability to apply its methods and techniques in pattern recognition and classification, data mining plays an important role in providing new and advanced methods to detect phishing attacks. In the following text, some of these studies will be discussed.…”

Section: Related Workmentioning

confidence: 99%

Proposing a new clustering method to detect phishing websites

Arab¹,

Sohrabi²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Phishing websites are fake ones that are developed by ill-intentioned people to imitate real and legal websites.Most of these types of web pages have high visual similarities to hustle the victims. The victims of phishing websites may give their bank accounts, passwords, credit card numbers, and other important information to the designers and owners of phishing websites. The increasing number of phishing websites has become a great challenge in e-business in general and in electronic banking specifically. In the present study, a novel framework based on model-based clustering is introduced to fight against phishing websites. First, a model is developed out of those websites that already have been identified as phishing websites as well as real websites that belong to the original owners. Then each new website is compared with the model and categorized into one of the model clusters by a probability. The analyses reveal that the proposed algorithm has high accuracy.

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“…In 2010, Dabbiru and Shashi [33] proposed CMP (Colossal Pattern Miner) algorithm for mining such patterns. Next, in 2012, Sohrabi and Barforoush [32] also proposed the BVBUC (Bitwise vertical bottom up colossal) algorithm for mining colossal patterns in high dimensional databases.…”

Section: Motivation and Goal Of Dissertationmentioning

confidence: 99%