R. Uday Kiran 교수

In this paper we have proposed an improved approach to extract rare association rules. Rare association rules are the association rules containing rare items. Rare items are less frequent items. For extracting rare itemsets, the single minimum support (minsup) based approaches like Apriori approach suffer from ldquorare item problemrdquo dilemma. At high minsup value, rare itemsets are missed, and at low minsup value, the number of frequent itemsets explodes. To extract rare itemsets, an effort

Frequent patterns are an important class of regularities that exist in a transaction database. Certain frequent patterns with low minimum support (minsup) value can provide useful information in many real-world applications. However, extraction of these frequent patterns with single minsup-based frequent pattern mining algorithms such as Apriori and FP-growth leads to rare item problem. That is, at high minsup value, the frequent patterns with low minsup are missed, and at low minsup value, the

A temporal database is a collection of transactions, ordered by their timestamps. Discovering partial periodic itemsets in temporal databases has numerous applications. However, to the best of our knowledge, no work has considered finding these itemsets in temporal databases, despite that this type of data is very common in real-life. Discovering partial periodic itemsets in temporal databases is challenging. It requires defining (i) an appropriate measure to assess the periodic interestingness

Periodic-frequent pattern mining is a challenging problem of great importance in many applications. Most previous works focused on finding these patterns in binary temporal databases and did not take into account the quantities of items within the data. This paper proposes a novel model of fuzzy periodic-frequent pattern (FPFP) that may exist in a quantitative temporal database (QTD). Finding FPFPs in QTD is a non-trivial and challenging task due to its huge search space. A novel pruning techniq

Partial periodic patterns are an important class of regularities that exist in a time series. A key property of these patterns is that they can start, stop, and restart anywhere within a series. We classify partial periodic patterns into two types: (i) regular patterns−patterns exhibiting periodic behavior throughout a series with some exceptions and (ii) recurring patterns−patterns exhibiting periodic behavior only for particular time intervals within a series. Past studies on partial periodic

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, the successful industrial application of this model has been hindered by the problem of combinatorial explosion of patterns, that is the generation of too many redundant patterns, most of which may be useless to the user. To address this problem, this paper proposes a novel model of maximal periodic- frequent pattern that may exist in a temporal database. A new pattern-growth a

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of da