Pradeep Kumar

Bio: Pradeep Kumar is an academic researcher from Indian Institute of Management Lucknow. The author has contributed to research in topics: Cluster analysis & Recommender system. The author has an hindex of 13, co-authored 48 publications receiving 565 citations. Previous affiliations of Pradeep Kumar include Institute for Development and Research in Banking Technology & Indian Institute of Management Ahmedabad.

Rough clustering of sequential data

Abstract: This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation. Subsequent clusters are formed using the concept of constrained-similarity upper approximation wherein a condition of relative similarity is used as a merging criterion. We report results of experimentation on msnbc web navigation dataset that are intrinsically sequential in nature. We have compared the results of the proposed approach with that of the traditional hierarchical clustering algorithm using vector coding of sequences. The results establish the viability of the proposed approach. The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group. Such descriptions can help web miners to identify potential and meaningful groups of users.

A web recommendation system considering sequential information

Abstract: With the rapid growth of information technology, the current era is witnessing an exponential increase in the generation and collection of web data. Projecting the right information to the right person is becoming more difficult day by day, which in turn adds complexity to the decision making process. Recommendation systems are intelligent systems that address this issue. They are widely used in e-commerce websites to recommend products to users. Most of the popular recommendation systems consider only the content information of users and ignore sequential information. Sequential information also provides useful insights about the behavior of users. We have developed a novel system that considers sequential information present in web navigation patterns, along with content information. We also consider soft clusters during clustering, which helps in capturing the multiple interests of users. The proposed system has utilized similarity upper approximation and singular value decomposition (SVD) for the generation of recommendations for users. We tested our approach on three datasets, the MSNBC benchmark dataset, simulated dataset and CTI dataset. We compared our approach with the first order Markov model as well as random prediction model. The results validate the viability of our approach.

DNNRec: A novel deep learning based hybrid recommender system

Abstract: We propose a novel deep learning hybrid recommender system to address the gaps in Collaborative Filtering systems and achieve the state-of-the-art predictive accuracy using deep learning. While collaborative filtering systems are popular with many state-of-the-art achievements in recommender systems, they suffer from the cold start problem, when there is no history about the users and items. Further, the latent factors learned by these methods are linear in nature. To address these gaps, we describe a novel hybrid recommender system using deep learning. The solution uses embeddings for representing users and items to learn non-linear latent factors. The solution alleviates the cold start problem by integrating side information about users and items into a very deep neural network. The proposed solution uses a decreasing learning rate in conjunction with increasing weight decay, the values cyclically varied across epochs to further improve accuracy. The proposed solution is benchmarked against existing methods on both predictive accuracy and running time. Predictive Accuracy is measured by Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared. Running time is measured by the mean and standard deviation across seven runs. Comprehensive experiments are conducted on several datasets such as the MovieLens 100 K, FilmTrust, Book-Crossing and MovieLens 1 M. The results show that the proposed technique outperforms existing methods in both non-cold start and cold start cases. The proposed solution framework is generic from the outperformance on four different datasets and can be leveraged for other ratings prediction datasets in recommender systems.

Fuzzy based clustering algorithm for privacy preserving data mining

Abstract: Sharing of data among multiple organisations is required in many situations. The shared data may contain sensitive information about individuals which if shared may lead to privacy breach. Thus, maintaining the individual privacy is a great challenge. In order to overcome the challenges involved in data mining, when data needs to be shared, privacy preserving data mining (PPDM) has evolved as a solution. The objective of PPDM is to have the interesting knowledge mined from the data at the same time to maintain the individual privacy. This paper addresses the problem of PPDM by transforming the attributes to fuzzy attributes. Thus, the individual privacy is also maintained, as one cannot predict the exact value, at the same time, better accuracy of mining results is achieved. ID3 and Naive Bayes classification algorithms over three different datasets are used in the experiments to show the effectiveness of the approach.

Pattern Discovery Using Sequence Data Mining: Applications and Studies

Abstract: Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios.Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.

Alliance advantage : the art of creating value through partnering

Abstract: 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.