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Data Mining - Concepts and Techniques.

Principles and Practice of Structural Equation Modeling (2nd ed.)

Matrix Factorization Techniques for Recommender Systems

Partnerships between companies receive a great deal of attention from top managers and researchers at the time of their formation. This attention results largely from the common perception that the initial structuring of partnerships and establishment of common goals determines partnership outcomes and success. In Alliance Advantage, Doz and Hamel shift the focus away from deal making to the internal processes within the partnership and the unfolding interactions among partners that play an important and relatively unexplored role in shaping outcomes. Focusing on the underlying reasons why companies enter alliances and the processes by which they continually learn from their interactions and re-evaluate common--and individual--goals, the authors paint a sophisticated picture of alliance dynamics over time. The authors challenge organizations to define their objectives for alliance formulation and consider whether their own corporate culture provides an "alliance ready" atmosphere.

Alliance advantage : the art of creating value through partnering

Mining communities is fundamental to network science as it facilitates the analysis of modern information infrastructure. Though community detection in networks is a very active research area, most of the algorithms focus on homogenous networks. Homogeneous networks consist of only one type of nodes and one type of edges. However, real-world networks mostly exhibit multi-typed objects and relationships thus forming heterogeneous information networks. There have been only a few studies in the past which aim at revealing the hidden communities in heterogeneous information networks. In this paper, we propose an effective community detection algorithm based on incremental seed expansion mechanism. Our algorithm utilizes the concept of weighted path matrix and transforms the network from heterogeneous to a homogenous network. The central idea behind our algorithm is to identify good seeds and then iteratively expand them such that the conductance score of detected communities is minimized. Experiment on a real-world network establish the viability of proposed approach.

Community detection in heterogenous networks using incremental seed expansion

Clustering is a prominent technique in data mining applications. It generates groups of data points that are similar to each other in a given aspect. Each group has some inherent latent similarity which is computed using the similarity measures. Clustering web users based on navigational pattern has always been an interesting as well as a challenging task. A web user, based on its navigational pattern, may belong to multiple categories. Intrinsically, web user navigation pattern exhibits sequential property. When dealing with sequence data, a similarity measure should be chosen, which captures both the order as well as content information during computation of similarity among sequences. In this paper, we have utilized the Sequence and Set Similarity Measure S^{3}M with rough set based similarity upper approximation clustering algorithm to group web users based on their navigational patterns. The quality of cluster formed using rough set based clustering algorithm with S^{3}M measure has been compared with the well known clustering algorithm, Density based spatial clustering of applications with noise DBSCAN. The experimental results show the viability of our approach.

An alternative approach for clustering web user sessions considering sequential information

In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related metainformation, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset.

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Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

Privacy preserving graph publishing is gaining importance in recent times mainly because of inherent privacy issues existing in publishing graph and social network data. Therefore, graph structure needs to be anonymized before publishing. This study proposes anonymization of a graph using fuzzy sets to preserve the graph's privacy while maintaining the utility that can be derived from the graph. We have conducted the experiments on four different datasets, and the results suggest that the proposed approach would not only help in protecting the privacy of data but also in maintaining the quality of data for analysis. To check the robustness of the proposed approach, we have validated the effectiveness of the approach on five key community detection algorithms on three performance measures.

Privacy preserving graph publishing using fuzzy set

Personalized item ranking for recommending top-N items of interest to a user is an interesting and challenging problem in e-commerce. Researchers and practitioner are continuously trying to devise new methodologies to improve the accuracy of recommendations. Recommendation problem becomes more challenging for sparse binary implicit feedback, due to the absence of explicit signals of interest and sparseness of data. In this paper, we deal with the problem of the sparseness of data and accuracy of recommendations. To address the issue, we propose an interest diffusion methodology in heterogeneous information network for items to be recommended using the meta-information related to items. In this heterogeneous information network, graph regularized interest diffusion is performed to generate personalized recommendations of top-N items. For interest diffusion, personalized weight learning is performed for different meta-information object types in the network. The experimental evaluation and comparison of the proposed methodology with the state-of-the-art techniques using the real-world datasets show the effectiveness of the proposed approach

Pradeep Kumar

Papers

Community detection in heterogenous networks using incremental seed expansion

An alternative approach for clustering web user sessions considering sequential information

Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

Privacy preserving graph publishing using fuzzy set

Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking