Soft clustering from crisp clustering using granulation for mobile call mining

2012 12th International Conference on Hybrid Intelligent Systems (HIS)

Bhalchandra

Khamitkar

et al. 2012

Information granules allow us to abstract real world objects using their relevant attributes. Level of abstraction allows us to create a network of information granules that are connected to each other through a real-world relationship. For example, the phone calls are connected to the origin and destination phone numbers. This paper describes the relationship between clustering schemes by propagating the information through the connection between granules. We focus on the hierarchical granular graphs, where phone numbers represent the higher level (coarser) granules that are connected to phone calls which are lower level (finer) granules. The paper studies the effect of transferring crisp and fuzzy clustering schemes through the granular hierarchy. The fuzzy clustering is shown to provide a better technique for such a transfer. Rough clustering derived from fuzzy clustering is shown to be a good instrument for comparing crisp and fuzzy clustering schemes.

Section: Derivation Of Soft Clustering Schemesmentioning

confidence: 99%

Propagation of knowledge from crisp and soft clustering through a granular hierarchy

2012 12th International Conference on Hybrid Intelligent Systems (HIS)

Bhalchandra

Khamitkar

et al. 2012

“…4) The deriving of the rough clusters from the possibilistic membership: The ratio defined in [27], [28] is used to indicate the boundary regions (the region of each peripheral object). The ratio uses the final ω ij .…”

Section: B the Rough Possibilistic K-modes: Rpkmmentioning

confidence: 99%

“…To correctly interpret the value of the ratio, we should set a new parameter which is the threshold≥1 [27], [28] denoted by T . If ratio ij ≤ T , we can conclude that the object i belongs to the upper bound of the cluster j.…”

Section: B the Rough Possibilistic K-modes: Rpkmmentioning

confidence: 99%

Rough possibilistic meta-clustering of retail datasets

Ammar

Elouedi

2014 International Conference on Data Science and Advanced Analytics (DSAA)

2014

In this paper, we develop a new meta-clustering approach using possibility and rough set theories to handle imperfection in real-world retail datasets. Our proposal is a soft meta-clustering approach that provides a framework for handling uncertainty in the belonging of an object to different clusters. The soft meta-clustering approach is based on the kmodes algorithm devoted for categorical data. Possibility theory is used to represent the uncertainty between objects and clusters through possibilistic membership degrees. Rough set theory is applied to indicate clusters with rough boundaries. The metaclustering consists of double clustering a retail dataset that contains customer and product data. The meta-clustering is improved by the application of the possibility and rough set theories. An initial clustering of the customer data is performed. Then, a second clustering of product data using the results of the first clustering is applied. The two clustering schemes then evolve iteratively affecting each other recursively. We detail our results and describe the structure of the final clusters of customers and products to prove the effectiveness of our proposal.

“…The use of the mobile phone data set allows the detection of the main characteristics of phone numbers (corresponding to the callers). There are the following four profiles of callers: Profile 1 (for cluster 1): It describes users with the highest use of mobile phone (including voice calls, messages, and packet data).…”

Section: Experiments With Real‐world Data Setsmentioning

confidence: 99%

Semantically Segmented Clustering Based on Possibilistic and Rough Set Theories

Ammar

Elouedi

2015

Int. J. Intell. Syst.

This paper reports the application of a possibility and rough set based clustering to semantically segmented real-world databases. The approach is an improved version of the well-known kmodes algorithm. It is a soft clustering method that clusters instances with uncertain categorical values to different clusters using their membership degrees. The possibility theory is used for dealing with uncertainty in the values of attributes and in the memberships of clusters. Rough sets are used to detect clusters with rough boundaries. We demonstrate the effectiveness of the proposed approach with the help of two real-world databases: a retail store or transactions data set and a mobile phone data set. The numeric values of attributes are segmented into semantically meaningful linguistic values using a novel discretization method. These linguistic values can lead to more natural interpretation of knowledge using possibilistic degrees. The possibilistic degrees describe our knowledge relative to the values of attributes (fully plausible to occur, may occur, or rejected) and identify the level of uncertainty in memberships to different clusters. In addition, our method deduces peripheral objects by calculating the approximate sets as defined in the rough set theory. The k-modes enhanced with rough set and possibility theories can provide semantically meaningful information for decision making to the store owners (retails data set) and telecommunication companies (mobile phone data set). C 2015 Wiley Periodicals, Inc.This section reports an overview of the possibility and rough set theories used in our study. Possibility theory is characterized by the use of dual set functions, namely possibility (Equation 5) and necessity (Equation 6) measures.N (E) = min ω / ∈ (1 − π (ω)) ,where E is an event.Here y ∈ U , and B(y) is a subset of U such that ∀x ∈ B(y) values of attributes in B are the same.