The problem of mining frequent patterns in non-temporal databases is studied extensively.
Conventional frequent pattern algorithms are not applicable to find temporal frequent items …

Temporal database is a database which captures and maintains past, present and future
data. Conventional databases are not suitable for handling such time varying data. In this …

Mining patterns in a market-basket dataset is a well-stated problem. There are a number of
approaches to deal with this problem. Different types of patterns may be present in a dataset …

Data mining and knowledge discovery is a fast growing field of study. Numerous
applications of data mining are reported on various domains. Due to significant …

Since the beginning, for the last forty five years, it has been accepted that fuzziness and
randomness are independent concepts. Workers in fuzzy mathematics have been trying to …

Two laws of randomness are necessary and sufficient to describe a normal fuzzy number.
Trying to impose one single probability law on an interval on which a possibility law has …

A large class of problems deals with temporal data. Identifying temporal patterns in these
datasets is a natural as well as an important task. In the recent time, researchers have …

Mining knowledge from time series database is always a challenging task due to inherent
complexity. Periodicity mining is one of the methods for analysing time series data in order to …

The problem of finding association rules from a dataset is to find all possible associations
that hold among the items, given a minimum support value and a minimum confidence. This …

Sequential patterns mining from data is a well stated data mining problem. It has a number
of applications such as DNA sequencing, signal processing, speech analysis etc. In this …