Abstract

Most data mining research has focused on devel-oping algorithms to discover statistically significant patternsin large datasets. However, utilizing the resulting patterns indecision support is more of an art, and the utility of such patterns is often questioned. In this paper we formalize a technique that utilizes data mining concepts to recommend an optimal set of items to customers in a retail store based on the contents of their market baskets. The recommended set of items maximizes the expected profit of the store and is decided based on patterns learnt from past transactions. In addition to concepts of clustering and frequent itemsets, the proposed method also combines the idea of knapsack problems to decide on the items to recommend. We empirically compare our approach with existing methods on both real and synthetic datasets and show that our method yields better profits while being faster and simpler.

Abstract

This paper presents a novel architecture PERIC-ASA, PER turbed frequent Itemset based classification for Com-putational Auditory Scene Analysis(CASA). A novel approach for perception of sound waves has been developed. Our aim is to develop a classifier whichcan correctly identify sound waves from noisy sound mixturesi.e. to solve the classical‘Cocktail Party Problem’. The architecture is based on Gestalt principles of grouping like Pragnanz, Proximity, Common Fateand Similarity. These grouping cues are incorporated into a new Classification approach which is based on a concept namely Perturbed Frequent Itemsets. The primary idea is more the ease with which we can identify different feature values, easier it is to identify the sound wave.

Abstract

Conventional Association Rule Mining (ARM) algorithms usually deal with datasets with binary values, and expect any numerical values to be converted to binary ones using sharp partitions, like Age = 25 to 60. In order to mitigate this constraint, Fuzzy logic is used to convert quantitative values of attributes to binary ones, so as to eliminate any loss of information arising due to sharp partitioning, especially at partition boundaries, and then generate fuzzy association rules. But, before any fuzzy ARM algorithm can be used, the original dataset (with crisp attributes) needs to be transformed into a form with fuzzy attributes. This paper describes a methodology, called FPrep, to do this pre-processing, which first involves using fuzzy clustering to generate fuzzy partitions, and then uses these partitions to get a fuzzy version (with fuzzy records) of the original dataset. Ultimately, the fuzzy data (fuzzy records) are represented in a standard manner such that they can be used as input to any kind of fuzzy ARM algorithm, irrespective of how it works and processes fuzzy data. We also show that FPrep is much faster than other such comparable transformation techniques, which in turn depend on non-fuzzy techniques, like hard clustering (CLARANS and CURE). Moreover, we illustrate the quality of the fuzzy partitions generated using FPrep, and the number of frequent itemsets generated by a fuzzy ARM algorithm when preceded by FPrep.

Abstract

All associative classifiers developed till now are crisp in nature, and thus use sharp partitioning to transform numerical attributes to binary ones like “Income = [100K and above]”. On the other hand, the novel fuzzy associative classification algorithm called FACISME, which we propose in this paper, uses fuzzy logic to convert numerical attributes to fuzzy attributes, like “Income = High”, thus maintaining the integrity of information conveyed by such numerical attributes. Moreover, FACISME is based on maximum entropy, and uses iterative scaling, both of which lend a very strong theoretical foundation to the algorithm. Entropy is one of the best measures of information, and maximum-entropy-based algorithms do not assume independence of parameters in the classification process. Thus, FACISME provides very goodaccuracy, and can work with all types of datasets (irrespective of size and type of attributes – numerical or binary) and domains.

Abstract

Evolutionary clustering is an emerging research area ad-dressing the problem of clustering dynamic data. An evolu-tionary clustering should take care of two conflicting criteria:preserving the current cluster quality and not deviating too much from the recent history. In this paper we propose an al-gorithm for evolutionary clustering using frequent itemsets.A frequent itemset based approach for evolutionary cluster-ing is natural and it automatically satisfy the two criteria of evolutionary clustering. We provide theoretical as well as experimental proofs to support our claims. We performed experiments on our approach using different datasets and the results show that our approach is comparable to most of the existing algorithms for evolutionary clustering

Abstract

The problem of regression is to estimate the value of a de-pendent numeric variable based on the values of one or more independent variables. Regression algorithms are used for prediction (including fore-casting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Although this problem has been studied exten-sively, most of these approaches are not generic in that they require the user to make an intelligent guess about the form of the regression equa-tion. In this paper we present a new regression algorithm PAGER – Pa-rameterless, Accurate, Generic, Efficient kNN-based Regression. PAGER is also simple and outlier-resilient. These desirable features make PAGER a very attractive alternative to existing approaches. Our experimental study compares PAGER with 12 other algorithms on 4 standard real datasets, and shows that PAGER is more accurate than its competitors.

Abstract

In this paper, we consider the problem of mining the special proper-ties of a given record in a relational dataset. In our formulation, a property is a combination of multiple attribute-value pairs. The support of a property is the number of records that satisfy it. We consider a property as special if its support occurs to us as a shockand the measure of this shock factor is more than a userdefined thresholdη. We provide a way to define this notion of shock based on en-tropy. We also output the shock factor for records in the dataset in a convenient,easily-interpretable manner. An illustrated example is provided on how users can interpret the results. Experiments on real and synthetic data sets reveal interesting properties of data records that cannot be mined using traditional approaches.

Abstract

High dimensionality is a major challenge in document clus-tering. Some of the recent algorithms address this problem by using frequent itemsets for clustering. But, most of these algorithms neglect the semantic relationship between the words. On the other hand there are algorithms that take care of the semantic relations between the words by making use of external knowledge contained in WordNet, Mesh,Wikipedia, etc but do not handle the high dimensionality. In this paper we present an efficient solution that addresses both these problems. We propose a hierarchical clustering algorithm using closed frequent itemsets that use Wikipedia as an external knowledge to enhance the document representation. We evaluate our methods based on F-Score on standard datasets and show our results to be better than existing approaches.

Abstract

Regression algorithms are used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Statistical approaches although popular, are are not generic in that they require the user to make an intelligent guess about the form of the regression equation. In this paper we present a new regression algorithm GEAR – Generic, Efficient, Accurate kNN-based Regression. In addition to this, GEAR is simple and outlier-resilient. These desirable features make GEAR a very attractive alternative to existing approaches. Our experimental study compares GEAR with fourteen other algorithms on five standard real datasets, and shows that GEAR is more accurate than all its competitors.

Abstract

This paper presents Perturbed Frequent Itemset based Classification Technique (PERFICT),a novel associative classification approach based on perturbed frequent itemsets. Most of the existing associative classifiers work well on transactional data where each record contains a set of boolean items.They are not very effective in general for relational data that typically contains real valued attributes.In PERFICT, we handle real attributes by treating items as (attribute,value) pairs, where the value is not the original one, but is perturbed by a small amount and is a range based value. We also propose our own similarity measure which captures the nature of real valued attributes and provide effective weights for the itemsets. The probabilistic contributions of different itemsets is taken into considerations during classification. Some of the applications where such a technique is useful are in signal classification, medical diagnosis and handwriting recognition. Experiments conducted on the UCI Repository datasets show that PERFICT is highly competitive in terms of accuracy in comparison with popular associative classification methods.