Krishna Kummamuru

Fuzzy co-clustering of documents and keywords

Conventional clustering algorithms such as K-means and SAHN (also known as AHC) have been well studied and used in the information retrieval community for clustering text documents. More recently, efforts have been made to cluster documents and words simultaneously. The FCCM algorithm due to Oh et al. is a fuzzy clustering algorithm that maximizes the co-occurrence of categorical attributes (keywords) and the individual patterns (documents) in clusters. However, this algorithm poses certain problems when the number of documents or the number of words is very large. In this paper, we modify the FCCM algorithm so that it can be used to cluster large text corpora. Our experiments show that the modified algorithm is scalable and produces meaningful clusters. We also show the relation between FCCM and the Spherical K-Means (SKM) algorithm and introduce the Spherical Fuzzy c-Means (SFCM) algorithm.

Automatic taxonomy generation: issues and possibilities

Automatic taxonomy generation deals with organizing text documents in terms of an unknown labeled hierarchy. The main issues here are (i) how to identify documents that have similar content, (ii) how to discover the hierarchical structure of the topics and subtopics, and (iii) how to find appropriate labels for each of the topics and subtopics. In this paper, we review several approaches to automatic taxonomy generation to provide an insight into the issues involved. We also describe how fuzzy hierarchies can overcome some of the problems associated with traditional crisp taxonomies.

Efficient online top-K retrieval with arbitrary similarity measures

The top-k retrieval problem requires finding k objects most similar to a given query object. Similarities between objects are most often computed as aggregated similarities of their attribute values. We consider the case where the similarities between attribute values are arbitrary (non-metric), due to which standard space partitioning indexes cannot be used. Among the most popular techniques that can handle arbitrary similarity measures is the family of threshold algorithms. These were designed as middleware algorithms that assume that similarity lists for each attribute are available and focus on efficiently merging these lists to arrive at the results. In this paper, we explore multi-dimensional indexing of non-metric spaces that can lead to efficient pruning of the search space utilizing inter-attribute relationships, during top-k computation. We propose an indexing structure, the AL-Tree and an algorithm to do top-k retrieval using it in an online fashion. The ALTree exploits the fact that many real world attributes come from a small value space. We show that our algorithm performs much better than the threshold based algorithms in terms of computational cost due to efficient pruning of the search space. Further, it out-performs them in terms of IOs by upto an order of magnitude in case of dense datasets.

Designing a voice-based employment exchange for rural India

We present observations from various user studies that have helped to guide the design and deployment of a voice-based employment platform. Specifically, we present findings as a function of demographics, considering employers and candidates across two regions of Karnataka. Our results are of use to those building interfaces to address rural unemployment in particular, and those looking to understand the employment landscape of rural India in general. Our main contributions are motivating the need for such a platform in this context, and offering insight into the engineering of job matches.

On-line evolutionary resource matching for job scheduling in heterogeneous grid environments

In this paper, we describe a resource matcher (RM) developed for the on-line resource matching in heterogeneous grid environments. RM is based on the principles of evolutionary algorithms (EA) and supports dynamic resource sharing, job priorities and preferences, job dependencies on multiple resource types, and resource specific and site-wide policies. We describe the evolutionary algorithm and the models used for representing the resource requirements, preferences, and policies. We evaluate three different methods for bootstrapping RM. We then describe a evolutionary matcher (EM) service

Learning spatially variant dissimilarity (SVaD) measures

a Web service implementation of RM for on-line resource matching. Preliminary performance results indicate that the EM service is efficient in speed and accuracy and can keep up with high job arrival rates - an important criterion for on-line resource matching systems. The service oriented architecture makes the EM service scalable and extensible and can be integrated with already existing grid services in a straightforward manner

Mining conversational text for procedures with applications in contact centers

Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed (dis)similarity measure between the data points in feature space. This makes the type of clusters identified highly dependent on the assumed similarity measure. Building on recent work in this area, we formally define a class of spatially varying dissimilarity measures and propose algorithms to learn the dissimilarity measure automatically from the data. The idea is to identify clusters that are compact with respect to the unknown spatially varying dissimilarity measure. Our experiments show that the proposed algorithms are more stable and achieve better accuracy on various textual data sets when compared with similar algorithms proposed in the literature.

Fuzzy targeting of customers based on product attributes

Many organizations provide dialog-based support through contact centers to sell their products, handle customer issues, and address product-and service-related issues. This is usually provided through voice calls—of late, web-chat based support is gaining prominence. In this paper, we consider any conversational text derived from web-chat systems, voice recognition systems etc., and propose a method to identify procedures that are embedded in the text. We discuss here how to use the identified procedures in knowledge authoring and agent prompting. In our experiments, we evaluate the utility of the proposed method for agent prompting. We first cluster the call transcripts to find groups of conversations that deal with a single topic. Then, we find possible procedure-steps within each topic-cluster by clustering the sentences within each of the calls in the topic-cluster. We propose a measure for differentiating between clusters that are procedure-steps and those that are topical sentence collections. Once we identify procedure-steps, we represent the calls as sequences of procedure-steps and perform mining to extract distinct and long frequent sequences which represent the procedures that are typically followed in calls. We show that the extracted procedures are comprehensive enough. We outline an approach for retrieving relevant procedures for a partially completed call and illustrate the utility of distinct collections of sequences in the real-world scenario of agent prompting using the retrieval mechanism.

Evaluating public service delivery in emerging markets

Finding the right set of target customers for a given product is an important problem in business-to-consumer (B2C) e-commerce. We propose an approach to solve this problem based on product attributes. Our approach is based on the assumption that the interest of a customer in a product depends on the customers interest towards its attributes. We use a collection of membership estimators to compute the degree to which a customer might be interested in a product. Each estimator is based on a particular product attribute, and takes into consideration purchase information about other products that are similar to the given one from the point of view of the attribute. Thus, each estimator generates a membership value for each customer in the target customer set based on a specific attribute. We then use these memberships and other customer information to predict the target customers. Our experimental results show a significant improvement in prediction accuracy when the proposed approach is used.

Reducing defects in IT service delivery

In the recent past, the public sector has been under considerable pressure to reform itself. Rising consumer expectations aided by enhancements in social media, information and communication technologies and demand for transparency have added pressure on public service agencies to make the process more inclusive and participatory. Governments across the world are therefore constantly trying to improve the quality and content of services being delivered to their citizens. Transformation in public sector delivery will not be sustainable and transitory unless the restructuring efforts are linked to performance evaluation. Recent studies have identified gaping holes in evaluating public service delivery via conventional approaches. In this paper, we propose a novel framework in the form of a capability maturity model to evaluate public service delivery. Our model maps the various factors associated with a public service in order to determine its maturity level. In order to assess the effectiveness of our proposed framework, we chose two public services as case studies. As part of our case study, we analyzed the maturity level and also proposed changes to improve these two public services.