ICAI-SR: Item Categorical Attribute Integrated Sequential Recommendation

2021 ◽  
Author(s):  
Xu Yuan ◽  
Dongsheng Duan ◽  
Lingling Tong ◽  
Lei Shi ◽  
Cheng Zhang
Keyword(s):  
Categorical Attribute

CLUSTERING CATEGORICAL AND NUMERICAL DATA: A NEW PROCEDURE USING MULTIDIMENSIONAL SCALING

2003 ◽  
Vol 02 (01) ◽  
pp. 135-159 ◽  
Author(s):  
SUNG-GI LEE ◽  
DEOK-KYUN YUN
Keyword(s):  
Multidimensional Scaling ◽  
Clustering Algorithms ◽  
Numerical Data ◽  
Large Data ◽  
Careful Analysis ◽  
Mixed Data ◽  
Coordinate Space ◽  
Data Sets ◽  
Categorical Attributes ◽  
Categorical Attribute

In this paper, we present a concept based on the similarity of categorical attribute values considering implicit relationships and propose a new and effective clustering procedure for mixed data. Our procedure obtains similarities between categorical values from careful analysis and maps the values in each categorical attribute into points in two-dimensional coordinate space using multidimensional scaling. These mapped values make it possible to interpret the relationships between attribute values and to directly apply categorical attributes to clustering algorithms using a Euclidean distance. After trivial modifications, our procedure for clustering mixed data uses the k-means algorithm, well known for its efficiency in clustering large data sets. We use the familiar soybean disease and adult data sets to demonstrate the performance of our clustering procedure. The satisfactory results that we have obtained demonstrate the effectiveness of our algorithm in discovering structure in data.

scholarly journals A Comparison of Categorical Attribute Data Clustering Methods

2014 ◽  
pp. 53-62 ◽  
Author(s):  
Ville Hautamäki ◽  
Antti Pöllänen ◽  
Tomi Kinnunen ◽  
Kong Aik Lee ◽  
Haizhou Li ◽  
...  
Keyword(s):  
Data Clustering ◽  
Clustering Methods ◽  
Attribute Data ◽  
Categorical Attribute

The impact of country of origin on context effects in choice

2017 ◽  
Vol 34 (6) ◽  
pp. 706-734 ◽  
Author(s):  
Moon-Yong Kim ◽  
Byung Il Park
Keyword(s):  
Context Effects ◽  
Country Of Origin ◽  
Content Type ◽  
Set Size ◽  
Choice Context ◽  
Core Set ◽  
Categorical Attribute ◽  
Asymmetric Dominance ◽  
The Impact ◽  
Dominance Set

Purpose The purpose of this paper is to examine the effect of country of origin (COO) information as an important/salient categorical attribute on choice context effects. Specifically, this research examines whether the introduction of a unique COO in the choice set will have a differential influence on context effects depending on the relative position of the third option (the asymmetrically dominated option vs the extreme option). Design/methodology/approach Five experiments were conducted in this research. Study 1 had a 2 (set size: two-option core set vs three-option asymmetric dominance set)×2 (competitor’s COO: common vs unique) between-subjects design. Study 2 had a 2 (set size: two-option core set vs three-option extreme option set)×2 (competitor’s COO: common vs unique) between-subjects design. To address the robustness of the effects, Studies 3-5 replicated the results of Studies 1 and 2. The data were analyzed by χ2 tests and logistic regression analyses. Findings The current research demonstrates that the attraction effect is attenuated by the introduction of a unique COO information in the competing option, whereas the tendency to prefer a middle option is not significantly affected. Originality/value The present research adds to the current understanding and the practical relevance of COO effects and context effects in marketing by examining the impact of COO as an important/salient categorical attribute on context effects.

scholarly journals A Novel Categorical Data Attribute Split Technique in Decision Tree Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 1607-1612
Keyword(s):  
Decision Tree ◽  
Categorical Data ◽  
Ease Of Use ◽  
Decision Tree Learning ◽  
Current Node ◽  
Categorical Attributes ◽  
Categorical Attribute ◽  
A New Technique ◽  
Class Labels ◽  
Better Than

A new technique is proposed for splitting categorical data during the process of decision tree learning. This technique is based on the class probability representations and manipulations of the class labels corresponding to the distinct values of categorical attributes. For each categorical attribute aggregate similarity in terms of class probabilities is computed and then based on the highest aggregated similarity measure the best attribute is selected and then the data in the current node of the decision tree is divided into the number of sub sets equal to the number of distinct values of the best categorical split attribute. Many experiments are conducted using this proposed method and the results have shown that the proposed technique is better than many other competitive methods in terms of efficiency, ease of use, understanding, and output results and it will be useful in many modern applications.

Clustering Categorical Data with k-Modes

2011 ◽  
pp. 246-250 ◽  
Author(s):  
Joshua Zhexue Huang
Keyword(s):  
Real World ◽  
Categorical Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Chemical Information ◽  
New Techniques ◽  
Small Set ◽  
Categorical Attributes ◽  
Categorical Attribute ◽  
Numeric Data

A lot of data in real world databases are categorical. For example, gender, profession, position, and hobby of customers are usually defined as categorical attributes in the CUSTOMER table. Each categorical attribute is represented with a small set of unique categorical values such as {Female, Male} for the gender attribute. Unlike numeric data, categorical values are discrete and unordered. Therefore, the clustering algorithms for numeric data cannot be used to cluster categorical data that exists in many real world applications. In data mining research, much effort has been put on development of new techniques for clustering categorical data (Huang, 1997b; Huang, 1998; Gibson, Kleinberg, & Raghavan, 1998; Ganti, Gehrke, & Ramakrishnan, 1999; Guha, Rastogi, & Shim, 1999; Chaturvedi, Green, Carroll, & Foods, 2001; Barbara, Li, & Couto, 2002; Andritsos, Tsaparas, Miller, & Sevcik, 2003; Li, Ma, & Ogihara, 2004; Chen, & Liu, 2005; Parmar, Wu, & Blackhurst, 2007). The k-modes clustering algorithm (Huang, 1997b; Huang, 1998) is one of the first algorithms for clustering large categorical data. In the past decade, this algorithm has been well studied and widely used in various applications. It is also adopted in commercial software (e.g., Daylight Chemical Information Systems, Inc, http://www. daylight.com/).

scholarly journals Clustering Behavioral Data for Advertising Purposes using K-Prototypes Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 2329-2334
Keyword(s):  
Social Media ◽  
Text Messaging ◽  
Clustering Algorithm ◽  
Behavioral Data ◽  
The Internet ◽  
Telemetry Data ◽  
Categorical Attribute ◽  
Cluster A ◽  
A Company ◽  
Potential Customers

Understanding the customer sentiment is very important when it comes to advertising. To appeal to their current and potential customers, a company must understand the market interests. Companies can segment their customers by using surveys and telemetry data to get to know the customer’s interests. One way of segmenting the customer is by grouping or clustering them according to their interests and behaviors. In this study, the k-prototypes clustering algorithm, which is an improved combination of k-means and k-modes algorithm, will be used to cluster a behavioral data that contains both numerical and categorical attribute, obtained from a survey conducted on teenagers into clusters of 4, 5, and 6. Each cluster will contain teenagers with certain behavior different from other clusters. And then by analyzing the results, advertisers will be able to define a profile that indicates their interests regarding the internet, social media and text messaging, effectively revealing the kind of ad that would be relatable for them.

Sign in / Sign up

Export Citation Format

Share Document