Categorical Data Visualization and Clustering Using Subjective Factors

Chang, Chia‐Hui; Ding, Zhi-Kai

doi:10.1007/978-3-540-30076-2_23

Cited by 12 publications

(12 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years there has been an increasing interest to analyze categorical data in a data warehouse context where data sets are rather large and may have a high number of categorical dimensions [4,6,8,15]. However, many traditional techniques associated to the exploration of data sets assume the attributes have continuous data (covariance, density functions, PCA, etc.).…”

Section: The Need To Encodementioning

confidence: 99%

Clustering of Heterogeneously Typed Data with Soft Computing - A Case Study

Kuri-Morales

Trejo-Baños

Cortés-Berrueco

2011

Advances in Soft Computing

View full text Add to dashboard Cite

Abstract. The problem of finding clusters in arbitrary sets of data has been attempted using different approaches. In most cases, the use of metrics in order to determine the adequateness of the said clusters is assumed. That is, the criteria yielding a measure of quality of the clusters depends on the distance between the elements of each cluster. Typically, one considers a cluster to be adequately characterized if the elements within a cluster are close to one another while, simultaneously, they appear to be far from those of different clusters. This intuitive approach fails if the variables of the elements of a cluster are not amenable to distance measurements, i.e., if the vectors of such elements cannot be quantified. This case arises frequently in real world applications where several variables (if not most of them) correspond to categories. The usual tendency is to assign arbitrary numbers to every category: to encode the categories. This, however, may result in spurious patterns: relationships between the variables which are not really there at the offset. It is evident that there is no truly valid assignment which may ensure a universally valid numerical value to this kind of variables. But there is a strategy which guarantees that the encoding will, in general, not bias the results. In this paper we explore such strategy. We discuss the theoretical foundations of our approach and prove that this is the best strategy in terms of the statistical behavior of the sampled data. We also show that, when applied to a complex real world problem, it allows us to generalize soft computing methods to find the number and characteristics of a set of clusters. We contrast the characteristics of the clusters gotten from the automated method with those of the experts.

show abstract

Section: The Need To Encodementioning

confidence: 99%

Clustering of Heterogeneously Typed Data with Soft Computing - A Case Study

Kuri-Morales

Trejo-Baños

Cortés-Berrueco

2011

Advances in Soft Computing

View full text Add to dashboard Cite

show abstract

“…Many algorithms have been proposed in recent years for clustering categorical data [26][27][28][29][30][31]. In [1], an association rule based clustering method is proposed for clustering customer transactions in a market database.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, much attention has been put on clustering categorical data [26][27][28][29][30][31], where data objects are made up of non-numerical attributes. Fast and accurate clustering of categorical data has many potential applications in customer relationship management, e-commerce intelligence, etc.…”

Section: Introductionmentioning

confidence: 99%

k-ANMI: A mutual information based clustering algorithm for categorical data

Deng

2008

Information Fusion

View full text Add to dashboard Cite

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering categorical data. The k-ANMI algorithm works in a way that is similar to the popular kmeans algorithm, and the goodness of clustering in each step is evaluated using a mutual information based criterion (namely, average normalized mutual information -ANMI) borrowed from cluster ensemble. This algorithm is easy to implement, requiring multiple hash tables as the only major data structure. Experimental results on real datasets show that k-ANMI algorithm is competitive with those state-of-the-art categorical data clustering algorithms with respect to clustering accuracy.

show abstract

“…Simultaneously, estimation of works has been discussed in terms of system lifecycle evaluation and estimation attributes (Daskalaki, Kopanas, Goudara, & Avouris, 2003). Additionally, the classification and clustering domains, such as visualization, web data search, position clustering, and graphs classification, have been extensively discussed (Chang & Ding, 2005;Coenen & Leng, 2007;Das & Datta, 2007;Nasraoui, Rojas, & Cardona, 2006).…”

Section: Introductionmentioning

confidence: 99%