Distance-based algorithms are widely used for data classification problems. The k-nearest
neighbour classification (k-NN) is one of the most popular distance-based algorithms. This …

Categorical attributes are ubiquitous in real-world collected data. However, such attributes
lack a well-defined distance metric and cannot be directly manipulated per algebraic …

To provide context-based personalized services utilizing smart appliances in a smart home
environment, we propose a framework for P erson A lized S ervice dis C overy U sing Fu …

Different clustering strategies to partition heterogeneous data set with numeric, binary,
categorical and ordinal attributes are explored by the researchers. All the real-life …

Retrieving the most suitable items and sorting them downwards from the best face many
challenges. The conformance measures are able to efficiently calculate similarities between …

A huge amount of data is generated daily by digital technologies such as social media, web
logs, traffic sensors, on-line transactions, tracking data, videos, and so on. This has led to the …

Many have explored various clustering strategies to partition heterogeneous datasets with
numeric, binary, nominal and ordinal attributes. The clustering algorithms seek to identify …

Searching for suitable entities in a datasets is still a challenging task, because the entities'
attributes are often expressed by various data types including numerical, categorical, and …

The web becomes an overwhelmingly huge repository of data. At the same time, users
demand access to the information on the web in a more natural way. In other words, users …

In this paper, we describe a method for evaluating suitable records from heterogeneous
datasets based on interval type-2 fuzzy sets (IT2FSs). Retrieving records from a dataset …